More stories

  • in

    Optimal settings and advantages of drones as a tool for canopy arthropod collection

    UAVs indeed proved to be a practical, efficient, and accurate tool in sampling insects within four different habitats in Quebec. Furthermore, different drone settings of speed, height, and net diameter may yield different insect orders, which can be useful in studies that aim to target specific insects. Nonetheless, only height, and not speed, net diameter or drone type influenced insect abundance. Compared with Lindgren funnels, drones were not only able to catch more insects in less time, but also a wider array of the insect community diversity.Our study successfully shows the promise of using drones to collect forest and wetland canopy arthropods. More arthropods were collected flying at zero meters (grazing the canopy) than flying at one meter, while different speed, net size and drone type had less of an effect on insect yield (Fig. 2). The one-meter setting was expected to yield different arthropod diversity, such as fewer terrestrial families (ex. Araneae) and more aerial families (ex. Diptera) compared to the grazing zero-meter setting. However, the proportions of the top three orders (Diptera, Hemiptera, and Araneae) were similar among settings (Fig. 3). The capture of arachnids at one meter above the canopy can be explained by webs that are attached to taller foliage in proximity to the area, or spiders ‘ballooning’ in the airspace on silk threads25. Because canopy height was not always uniform, flying while grazing the canopy underneath the drone was at times lower than other parts of the canopy. Another explanation could be jumping spiders (ex. family Salticidae) which have been found to react to a disturbance or threat by leaping, possibly into the drone net26. Though the main three orders were in similar proportion, the one-meter setting caught five fewer orders in total than the zero-meter setting did. Flying at one meter was the only setting that captured no insects of order Coleoptera, Hymenoptera, or Orthoptera, suggesting that these orders spend time in and among the wetland canopy, and are seldom above the grassy canopy (Fig. 3). Most importantly, this setting only caught nine insects total over all flights, revealing itself to be an inefficient method of insect collection. This can be due to the number of insects available to be collected at each height. When flying at one meter, the net has access to only aerial insects in flight above the canopy (ex. flies). Flying while grazing the canopy, however, gives the researcher access to the same aerial insects in flight above the canopy, but also aerial insects in flight within the canopy (ex. bees), aerial insects at rest on the canopy (ex. leafhoppers), and terrestrial insects on the canopy (ex. ants). Thus, flying the drone while grazing the canopy opens the possibility of capturing three more insect groups compared to flying above the canopy. It is also possible that there are indeed many insects to be caught solely in the airspace, but that the ideal height for collecting insects strictly above the canopy is either less than or greater than one meter—which is the only height above the canopy that we tested.This sampling period caught three total insects from order Odonata, with two of the three being caught with the 18-inch diameter net setting (Fig. 3). As these dragonflies are typically fast flyers and of large body size, perhaps the extra diameter of the larger net was helpful in increasing the chances of catching Odonates, though we do not have enough data to make solid conclusions. This would be a valuable line of future research for studies focused on dragonflies, or other large and fast-flying insects.Flying the drone and hanging sweep net at 20 km/hr yielded the highest number and proportion of insects in the order Hemiptera, which are often found at rest within the canopy27. We speculate that the faster speed of the drone striking the grassy canopy more swiftly, thus giving the insects resting on the grasses less of an opportunity to evade the threat of the approaching net. Future studies targeting the collection of true bugs should utilize a faster drone speed in flight to optimize yield.With 84% of insects found within the second layer of our net, we conclude that our novel net design with two layers of tulle is satisfactory in retaining insects and preventing most from escaping when landing the drone. In addition to the insects counted, we never witnessed any insects flying out during landing stages. We believe that our methodology of flying the drone in quickly and covering the opening of the net with cardboard before landing the drone, in addition to the extra layer of netting, was successful at retaining the insects caught. Determining how to fly the drone and net over the two forest canopy habitats was a challenge. When flying, it was impossible for the drone camera to look both forward—to see obstacles coming up, and downwards—to see how close the net was hanging regarding the top of the canopy. For this reason, we used a second drone as a spotter for the first, the pilot of which could give instructions on moving up or down. Forest canopies were particularly difficult, as the height from one tree to the next was always different, the drone had to be constantly adjusted. We experienced many snags on branches, although they were not damaging to the net or drone. Once we became comfortable flying the drone low enough to graze the canopy, snagging became a common occurrence that was easily remedied. In fact, snagging the net probably helped in the collection of insects on those branches—a technique that could be honed and used in future studies using nets and drones over forest canopies.Over our 12 days of sampling habitat canopies with drones, we were able to determine that wetlands had the highest diversity and abundance of the four habitats examined, with lake habitats showing the lowest Shannon-Weiner Diversity index (H’), and the highest Pielou’s evenness index (J). It is unsurprising that lakes showed the most even distribution of families, as is often the case with habitats having low species richness, as there are less competitors that could dominate the habitat28. Habitat, humidity, and temperature were the most important variables affecting drone insect yield, with habitat being the common variable in all high scoring models. Wetlands had by the far the most insects collected, in addition to the highest diversity and species richness. This can be explained simply by the plant composition in wetlands compared to the other habitats. While coniferous and deciduous forests are dominated by a few species (and lakes have little to no vegetation over the water) wetlands can host a wide variety of plant species. Because insect diversity correlates with plant richness and abundance, wetlands can provide shelter and sustenance for many more groups of insects that the other habitats we studied29.Lindgren funnels disproportionately collected insects from order Coleoptera (Fig. 7). Although Lindgren funnels have been used in papers reporting results focused on insects of orders Hemiptera30,31,32,33 and Diptera34,35,36, it is unclear whether some were targeted studies or all simply bycatch of the funnel from other experiments. Instead, Lindgren funnels are overwhelmingly used in Coleoptera studies as the funnels resemble a tree and attracts various wood-boring beetles37,38,39,40,41. This attraction explains the large number and proportion of beetles caught in funnels in this study. However, diversity indices show that in three of four habitats, drones collect a higher diversity sample than the Lindgren funnels (Tables 1 and 2). Thus, though Lindgren funnels are undoubtedly effective at collecting beetles from the environment, our results indicate that the drone collection method is preferable when seeking an accurate representation of the insect diversity of the habitat. Studies focused on Coleoptera could also employ this method, which would be helpful in determining the status and proportion of beetles within the population and compared to other insect orders.In addition to the larger diversity collected by drones, the temporal advantage of this technique over the funnels can not be understated. During our study, it took three Lindgren funnel traps established for seven days to collect a total of 36 insects at the wetland sites (0.001 insect collected per minute). Comparatively, at the same height and placement, drones were able to collect 391 insects in only a combined 36 min (10.9 insects collected per minute) (Fig. 7). This large difference in both yield and time scale demonstrates that the drone collection method is vastly more efficient at arthropod sampling compared to the Lindgren funnels.While this study was successful at validating the usefulness of drones in canopy entomology studies and insect collection in general, it does have its limitations. Optimal drone settings were only examined at wetland grassy canopy sites, and it is possible that the drone might perform differently within different habitats. For example, grazing the canopy at 20 km/hr might result in high insect yield at wetlands, where the lack of obstacles made it relatively easy to fly quickly. But the same settings may be unrealistic and prone to net snagging when sampling over other habitats, such as the coniferous forest canopy. Furthermore, Lindgren funnels were an acceptable comparison to drone collection for yield and diversity at some habitats, however it was impossible to get the funnels up into the canopy where sampling took place at coniferous and deciduous sites. There is no doubt that the advantage of this method lies in its accessibility, speed, and safety—studies that need more precise and fine sampling might not benefit from drones.Overall, our research demonstrates that drones are an efficient and accurate tool in collecting a wide diversity of insects above the canopies of different habitats. Benefits included rapidly and safely sampling the airspace while drawbacks included battery life limiting the duration of sampling. If this new technique is integrated into the field of entomology, canopy studies can be done much more often, for less money, and more safely than they have been done using other techniques. In 2019, a review of the potential causes of decline of aerial insectivores concluded that insect declines and changes in high quality prey availability could be a large driver of insectivore declines9. However, there is a lack of research detailing insect trends over time. The drone collection method used in this study could provide the missing link between the need for more research of aerial canopy insects and the limitations of the current methodology in entomology. This technique can be used in conjunction with aerial insectivore surveys and diet studies to begin to determine the relationship between declining predators and prey. Future research may also use and add to our guidelines to customize drone and net settings for studies targeting specific insect orders or families. More

  • in

    Thermal acclimation and metabolic scaling of a groundwater asellid in the climate change scenario

    Li, J. & Thompson, D. W. Widespread changes in surface temperature persistence under climate change. Nature 599(7885), 425–430. https://doi.org/10.1038/s41586-021-03943-z (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Raftery, A. E., Zimmer, A., Frierson, D. M., Startz, R. & Liu, P. Less than 2 °C warming by 2100 unlikely. Nat. Clim. Change 7, 637–641 (2017).ADS 
    CAS 

    Google Scholar 
    Olabi, A. G. et al. Assessment of the pre-combustion carbon capture contribution into sustainable development goals SDGs using novel indicators. Renew. Sustain. Energy Rev. 153, 111710. https://doi.org/10.1016/j.rser.2021.111710 (2022).CAS 

    Google Scholar 
    Badino, G. Cave temperatures and global climatic change. Int. J. Speleol. 33(1), 103–114 (2004).
    Google Scholar 
    Wang, J. et al. Recent global decline in endorheic basin water storages. Nat. Geosci. 11(12), 926–932 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Figura, S., Livingstone, D. M., Hoehn, E. & Kipfer, R. Regime shift in groundwater temperature triggered by the Arctic Oscillation. Geophys. Res. Lett. 38(23), 401–405 (2011).
    Google Scholar 
    Mueller, M. H., Huggenberger, P. & Epting, J. Combining monitoring and modelling tools as a basis for city-scale concepts for a sustainable thermal management of urban groundwater resources. Sci. Total Environ. 627, 1121–1136 (2018).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Taylor, C. A. & Stefan, H. G. Shallow groundwater temperature response to climate change and urbanization. J. Hydrol. 375, 601–612 (2009).ADS 
    CAS 

    Google Scholar 
    Dehghani, R., Poudeh, H. T. & Izadi, Z. The effect of climate change on groundwater level and its prediction using modern meta-heuristic model. Ground. Sustain. Dev. 16, 100702. https://doi.org/10.1016/j.gsd.2021.100702 (2022).
    Google Scholar 
    Lenton, T. M. et al. Climate tipping points—Too risky to bet against. Nature 57, 592–595 (2019).ADS 

    Google Scholar 
    Albert, J. S. et al. Scientists’ warning to humanity on the freshwater biodiversity crisis. Ambio 50(1), 85–94 (2021).PubMed 

    Google Scholar 
    Stein, H. et al. Stygoregions—A promising approach to a bioregional classification of groundwater systems. Sci. Rep. 2, 673. https://doi.org/10.1038/srep00673 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Baković, N., Matoničkin Kepčija, R. & Siemensma, F. J. Transitional and small aquatic cave habitats diversification based on protist assemblages in the Veternica cave (Medvednica Mt., Croatia). Subterr. Biol. 42, 43–60 (2022).
    Google Scholar 
    Magnabosco, C. et al. The biomass and biodiversity of the continental subsurface. Nat. Geosci. 11(10), 707–717 (2018).ADS 
    CAS 

    Google Scholar 
    Chen, Z. et al. The World Karst Aquifer Mapping project: Concept, mapping procedure and map of Europe. Hydrogeol. J. 25, 771–785 (2017).ADS 

    Google Scholar 
    Eme, D. et al. Do cryptic species matter in macroecology? Sequencing European groundwater crustaceans yields smaller ranges but does not challenge biodiversity determinants. Ecography 41(2), 424–436 (2018).
    Google Scholar 
    Manenti, R. et al. The stenoendemic cave-dwelling planarians (Platyhelminthes, Tricladida) of the Italian Alps and Apennines: conservation issues. J. Nat. Conserv. 45, 90–97 (2018).
    Google Scholar 
    Zagmajster, M., Malard, F., Eme, D. & Culver, D. C. Subterranean biodiversity patterns from global to regional scales. In Cave Ecology, Ecological Studies—Analysis and Synthesis (eds Moldovan, O. et al.) 19–227 (Springer, 2018).
    Google Scholar 
    Hose, G. C. et al. Invertebrate traits, diversity and the vulnerability of groundwater ecosystems. Funct. Ecol. 36, 2200. https://doi.org/10.1111/1365-2435.14125 (2022).CAS 

    Google Scholar 
    Angilletta, M. J. Jr. & Angilletta, M. J. Thermal Adaptation: A Theoretical and Empirical Synthesis (Oxford University Press, 2009).
    Google Scholar 
    Pallarées, S. et al. Loss of heat acclimation capacity could leave subterranean specialists highly sensitive to climate change. Anim. Conserv. 24(3), 482–490 (2020).
    Google Scholar 
    Vasseur, D. A. et al. Increased temperature variation poses a greater risk to species than climate warming. Proc. R. Soc. B 281, 20132612. https://doi.org/10.1098/rspb.2013.2612 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Castaño-Sánchez, A., Hose, G. C. & Reboleira, A. S. P. Ecotoxicological effects of anthropogenic stressors in subterranean organisms: A review. Chemosphere 244, 125422. https://doi.org/10.1016/j.chemosphere.2019.125422 (2020).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Castaño-Sánchez, A., Hose, G. C. & Reboleira, A. S. P. Salinity and temperature increase impact groundwater crustaceans. Sci. Rep. 10(1), 1–9 (2020).
    Google Scholar 
    Issartel, J., Hervant, F., Voituron, Y., Renault, D. & Vernon, P. Behavioural, ventilatory and respiratory responses of epigean and hypogean crustaceans to different temperatures. Comp. Biochem. Physiol. Mol. Amp Integr. Physiol. 141, 1–7 (2005).
    Google Scholar 
    Issartel, J., Voituron, Y. & Hervant, F. Impact of temperature on the survival, the activity and the metabolism of the cave-dwelling Niphargus virei, the ubiquitous stygobiotic N. rhenorhodanensis and the surface-dwelling Gammarus fossarum (Crustacea, Amphipoda). Subterr. Biol. 5, 9–14 (2007).
    Google Scholar 
    Mermillod-Blondin, F. et al. Thermal tolerance breadths among groundwater crustaceans living in a thermally constant environment. J. Exp. Biol. 216, 1683–1694 (2013).CAS 
    PubMed 

    Google Scholar 
    Di Lorenzo, T. et al. Metabolic rates of a hypogean and an epigean species of copepod in an alluvial aquifer. Freshw. Biol. 60, 426–435 (2015).
    Google Scholar 
    Di Lorenzo, T. & Galassi, D. M. P. Effect of temperature rising on the stygobitic crustacean species Diacyclops belgicus: Does global warming affect groundwater populations? Water 9, 951. https://doi.org/10.3390/w9120951 (2017).ADS 
    CAS 

    Google Scholar 
    Mammola, S. et al. Climate change going deep: The effects of global climatic alterations on cave ecosystems. Anthr. Rev. 6(1–2), 98–116 (2019).
    Google Scholar 
    Jones, K. et al. The critical thermal maximum of diving beetles (Coleoptera: Dytiscidae): A comparison of subterranean and surface-dwelling species. Curr. Opin. Insect. Sci. 1, 100019 (2021).
    Google Scholar 
    Pörtner, H. O. Physiological basis of temperature-dependent biogeography: Trade-offs in muscle design and performance in polar ectotherms. J. Exp. Biol. 205, 2217–2230 (2022).
    Google Scholar 
    Clarke, A. & Fraser, K. P. P. Why does metabolism scale with temperature? Funct. Ecol. 18, 243–251 (2004).
    Google Scholar 
    Dell, A. I., Pawar, S. & Savage, V. M. Systematic variation in the temperature dependence of physiological and ecological traits. Proc. Natl. Acad. Sci. 108, 10591–10596 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Willmer, P., Stone, G. & Johnston, I. Environmental Physiology of Animals (Wiley, 2009).
    Google Scholar 
    Gillooly, J. F., Brown, J. H., West, G. B., Savage, V. M. & Charnov, E. L. Effects of size and temperature on metabolic rate. Science 293, 2248–2251 (2001).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gillooly, J. F., Charnov, E. L., West, G. B., Savage, V. M. & Brown, J. H. Effects of size and temperature on developmental time. Nature 417, 70–73 (2002).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Hervant, F., Mathieu, J., Barré, H., Simon, K. & Pinon, C. Comparative study on the behavioural, ventilatory, and respiratory responses of hypogean and epigean crustaceans to long-term starvation and subsequent feeding. Comp. Biochem. Physiol. B 118A, 1277–1283 (1997).CAS 

    Google Scholar 
    Wilhelm, F. M., Taylor, S. J. & Adams, G. L. Comparison of routine metabolic rates of the stygobite, Gammarus acherondytes (Amphipoda: Gammaridae) and the stygophile, Gammarus troglophilus. Freshwat. Biol. 51, 1162–1174 (2006).
    Google Scholar 
    Reboleira, A. S. P. S., Borges, P., Gonçalves, F., Serrano, A. R. M. & Oromí, P. The subterranean fauna of a biodiversity hotspot region—Portugal: An overview and its conservation. Int. J. Speleol. 40(1), 23–37 (2011).
    Google Scholar 
    Reboleira, A. S. P. S., Abrantes, N., Oromí, P. & Gonçalves, F. J. M. Acute toxicity of copper sulfate and potassium dichromate on stygobiont Proasellus: General aspects of groundwater ecotoxicology and future perspectives. Water Air Soil Pollut. 224, 1550. https://doi.org/10.1007/s11270-013-1550-0 (2013).ADS 
    CAS 

    Google Scholar 
    Morvan, C. et al. Timetree of Aselloidea reveals species diversification dynamics in groundwater. Syst. Biol. 62(4), 512–522 (2013).CAS 
    PubMed 

    Google Scholar 
    Castaño-Sánchez, A., Malard, F., Kalčikova, G. & Reboleira, A. S. P. S. Novel protocol for acute in situ ecotoxicity test using native crustaceans applied to groundwater ecosystems. Water 13(8), 1132. https://doi.org/10.3390/w13081132 (2021).CAS 

    Google Scholar 
    Di Lorenzo, T. et al. Recommendations for ecotoxicity testing with stygobiotic species in the framework of groundwater environmental risk assessment. Sci. Total Environ. 681(1), 292–304 (2019).ADS 
    MathSciNet 
    PubMed 

    Google Scholar 
    Rezende, E. L., Tejedo, M. & Santos, M. Estimating the adaptative potential of critical thermal limits: Methodological problems and evolutionary implications. Funct. Ecol. 25, 111–121 (2011).
    Google Scholar 
    Schneider, C. A., Rasband, W. S. & Eliceiri, K. W. NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671–675 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
    Google Scholar 
    Harvey, P. H. & Pagel, M. D. The Comparative Method in Evolutionary Biology (Oxford University Press, 1991).
    Google Scholar 
    Dodds, P. S., Rothman, D. H. & Weitz, J. S. Re-examination of the “3/4” law of metabolism. J. Theor. Biol. 209, 9–27 (2001).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Manly, B. F. J. Randomization, Bootstrap and Monte Carlo Methods in Biology (Chapman & Hall/CRC Press, 2006).MATH 

    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (R Foundation for Statistical Computing, Vienna, Austria, 2018).Simčič, T. & Sket, B. Comparison of some epigean and troglobiotic animals regarding their metabolism intensity. Examination of a classical assertion. Int. J. Speleol. 48, 133–144 (2019).
    Google Scholar 
    Hazell, S. P., Pedersen, B. P., Worland, M. R., Blackburn, T. M. & Bale, J. S. A method for the rapid measurement of thermal tolerance traits in studies of small insects. Physiol. Entomol. 33(4), 389–394 (2008).
    Google Scholar 
    Cohen, J. M., Lajeunesse, M. J. & Rohr, J. R. A global synthesis of animal phenological responses to climate change. Nat. Clim. Change 8, 224. https://doi.org/10.1038/s41558-018-0067-3 (2018).ADS 

    Google Scholar 
    Ficetola, G. F., Lunghi, E. & Manenti, R. Microhabitat analyses support relationships between niche breadth and range size when spatial autocorrelation is strong. Ecography 43(5), 724–734 (2020).
    Google Scholar 
    Sánchez-Fernández, D., Rizzo, V. & Bourdeau, C. The deep subterranean environment as a model system in ecological, biogeographical and evolutionary research. Subterr. Biol. 25, 1–7 (2018).
    Google Scholar 
    Pallarés, S. et al. Loss of heat acclimation capacity could leave subterranean specialists highly sensitive to climate change. Anim. Conserv. 24(3), 482–490 (2021).MathSciNet 

    Google Scholar 
    Griebler, C. & Avramov, M. Groundwater ecosystem services: A review. Freshw. Sci. 34(1), 355–367 (2015).
    Google Scholar 
    Saccò, M. et al. Stygofaunal diversity and ecological sustainability of coastal groundwater ecosystems in a changing climate: The Australian paradigm. Freshw. Biol. https://doi.org/10.1111/fwb.13987 (2022).
    Google Scholar 
    Ikeda, T., Kanno, Y., Ozaki, K. & Shinada, A. Metabolic rates of epipelagic marine copepods as a function of body mass and temperature. Mar. Biol. 139, 587–596 (2001).
    Google Scholar 
    Mezek, T., Simčič, T., Arts, M. T. & Brancelj, A. Effect of fasting on hypogean (Niphargus stygius) and epigean (Gammarus fossarum) amphipods: A laboratory study. Aquat. Ecol. 44(2), 397–408 (2010).CAS 

    Google Scholar 
    Hüppop, K. The role of metabolism in the evolution of cave animals. NSS Bulletin 47, 136–146 (1985).
    Google Scholar 
    Humphreys, W. F. Hydrogeology and groundwater ecology: Does each inform the other? Hydrogeol. J. 17(1), 5–21 (2009).ADS 
    CAS 

    Google Scholar 
    Glazier, D. S. The 3/4-power law is not universal: Evolution of isometric, ontogenetic metabolic scaling in pelagic animals. Bioscience 56(4), 325–332 (2006).
    Google Scholar 
    Sánchez-Fernández, D., Galassi, D. M. P., Wynne, J. J., Cardoso, P. & Mammola, S. Don’t forget subterranean ecosystems in climate change agendas. Nat. Clim. Change 11, 458–459 (2021).ADS 

    Google Scholar 
    Reboleira, A. S. P. S. et al. Nutrient-limited subarctic caves harbour more diverse and complex bacterial communities than their surface soil. Environ. Microbiome 17, 41 (2022).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns

    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

  • in

    Characterizing phenotypic diversity in marine populations of the threespine stickleback

    Bell, M. A. & Foster, S. A. The Evolutionary Biology of the Threespine Stickleback (Oxford University Press, 1994).
    Google Scholar 
    Seebacher, F., Webster, M. M., James, R. S., Tallis, J. & Ward, A. J. W. Morphological differences between habitats are associated with physiological and behavioural trade-offs in stickleback (Gasterosteus aculeatus). R. Soc. Open Sci. 3, 160316 (2016).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bolnick, D. I. et al. Phenotype-dependent native habitat preference facilitates divergence between parapatric lake and stream stickleback. Evolution 63, 2004–2016 (2009).PubMed 

    Google Scholar 
    Svanbäck, R. & Schluter, D. Niche specialization influences adaptive phenotypic plasticity in the threespine stickleback. Am. Nat. 180, 50–59 (2012).PubMed 

    Google Scholar 
    Caldecutt, W. J. & Adams, D. C. Morphometrics of trophic osteology in the threespine stickleback, Gasterosteus aculeatus. Copeia 1998, 827–838 (1998).
    Google Scholar 
    Yershov, P. & Sukhotin, A. Age and growth of marine three-spined stickleback in the White Sea 50 years after a population collapse. Polar Biol. 38, 1813–1823 (2015).
    Google Scholar 
    Dorgham, A. S. et al. Morphological variation of threespine stickleback (Gasterosteus aculeatus) on different stages of spawning period. Proc. KarRC RAS 59–73 (2018). https://doi.org/10.17076/them819.DeFaveri, J. & Merilä, J. Evidence for adaptive phenotypic differentiation in Baltic Sea sticklebacks. J. Evol. Biol. 26, 1700–1715 (2013).CAS 
    PubMed 

    Google Scholar 
    Shaw, K. A., Scotti, M. L. & Foster, S. A. Ancestral plasticity and the evolutionary diversification of courtship behaviour in threespine sticklebacks. Anim. Behav. 73, 415–422 (2007).
    Google Scholar 
    McGee, M. D., Schluter, D. & Wainwright, P. C. Functional basis of ecological divergence in sympatric stickleback. BMC Evol. Biol. 13, 277 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Berner, D., Grandchamp, A.-C. & Hendry, A. P. Variable progress toward ecological speciation in parapatry: Stickleback across eight lake-stream transitions. Evolution 63, 1740–1753 (2009).PubMed 

    Google Scholar 
    Walker, J. A. Ecological morphology of lacustrine threespine stickleback Gasterosteus aculeatus L. (Gasterosteidae) body shape. Biol. J. Linn. Soc. 61, 3–50 (1997).
    Google Scholar 
    Hagen, D. W. & Gilbertson, L. G. Geographic variation and environmental selection in Gasterosteus aculeatus L. in the Pacific Northwest America. Evolution 26, 32–51 (1972).CAS 
    PubMed 

    Google Scholar 
    Smith, C., Zięba, G., Spence, R., Klepaker, T. & Przybylski, M. Three-spined stickleback armour predicted by body size, minimum winter temperature and pH. J. Zool. 311, 13–22 (2020).
    Google Scholar 
    Aguirre, W. E. & Bell, M. A. Twenty years of body shape evolution in a threespine stickleback population adapting to a lake environment: Stickleback body shape evolution. Biol. J. Linn. Soc. 105, 817–831 (2012).
    Google Scholar 
    Lavin, P. A. & McPhail, J. D. The evolution of freshwater diversity in the threespine stickleback (Gasterosteus aculeatus): Site-specific differentiation of trophic morphology. Can. J. Zool. 63, 2632–2638 (1985).
    Google Scholar 
    Matthews, B., Marchinko, K. B., Bolnick, D. I. & Mazumder, A. Specialization of trophic position and habitat use by sticklebacks in an adaptive radiation. Ecology 91, 1025–1034 (2010).PubMed 

    Google Scholar 
    Lefébure, R., Larsson, S. & Byström, P. A temperature-dependent growth model for the three-spined stickleback Gasterosteus aculeatus. J. Fish Biol. 79, 1815–1827 (2011).PubMed 

    Google Scholar 
    Foster, S. A. Inference of evolutionary pattern: Diversionary displays of three-spined sticklebacks. Behav. Ecol. 5, 114–121 (1992).
    Google Scholar 
    Taylor, E. B. & McPhail, J. D. Evolutionary history of an adaptive radiation in species pairs of threespine sticklebacks (Gasterosteus): Insights from mitochondrial DNA. Biol. J. Linn. Soc. 66, 271–291 (1999).
    Google Scholar 
    Hohenlohe, P. A., Bassham, S., Currey, M. & Cresko, W. A. Extensive linkage disequilibrium and parallel adaptive divergence across threespine stickleback genomes. Phil. Trans. R. Soc. B 367, 395–408 (2012).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Walker, J. A. & Bell, M. A. Net evolutionary trajectories of body shape evolution within a microgeographic radiation of threespine sticklebacks (Gasterosteus aculeatus). J. Zool. 252, 293–302 (2000).
    Google Scholar 
    Kristjánsson, B. K., Skúlason, S. & Noakes, D. L. G. Rapid divergence in a recently isolated population of threespine stickleback (Gasterosteus aculeatus L.). Evol. Ecol. Res. 4, 659–672 (2002).
    Google Scholar 
    Wund, M. A., Baker, J. A., Clancy, B., Golub, J. L. & Foster, S. A. A test of the “flexible stem” model of evolution: Ancestral plasticity, genetic accommodation, and morphological divergence in the threespine stickleback radiation. Am. Nat. 172, 449–462 (2008).PubMed 

    Google Scholar 
    Arif, S., Aguirre, W. E. & Bell, M. A. Evolutionary diversification of opercle shape in Cook Inlet threespine stickleback. Biol. J. Linn. Soc. 97, 832–844 (2009).
    Google Scholar 
    Terekhanova, N. V. et al. Fast evolution from precast bricks: Genomics of young freshwater populations of threespine stickleback Gasterosteus aculeatus. PLoS Genet. 10, e1004696 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Miller, S. E., Roesti, M. & Schluter, D. A single interacting species leads to widespread parallel evolution of the stickleback genome. Curr. Biol. 29, 530–537 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ab Ghani, N. I., Herczeg, G. & Merilä, J. Effects of perceived predation risk and social environment on the development of three-spined stickleback (Gasterosteus aculeatus) morphology. Biol. J. Linn. Soc. 118, 520–535 (2016).
    Google Scholar 
    DeFaveri, J. & Merilä, J. Local adaptation to salinity in the three-spined stickleback?. J. Evol. Biol. 27, 290–302 (2014).CAS 
    PubMed 

    Google Scholar 
    Jakubavičiūtė, E., De Blick, Y., Dainys, J., Ložys, L. & Olsson, J. Morphological divergence of three-spined stickleback in the Baltic Sea—Implications for stock identification. Fish. Res. 204, 305–315 (2018).
    Google Scholar 
    Yanos, C. L. et al. Predator biomass and vegetation influence the coastal distribution of threespine stickleback morphotypes. Ecol. Evol. 00, 1–12 (2021).
    Google Scholar 
    Fang, B., Merilä, J., Ribeiro, F., Alexandre, C. M. & Momigliano, P. Worldwide phylogeny of three-spined sticklebacks. Mol. Phylogenet. Evol. 127, 613–625 (2018).PubMed 

    Google Scholar 
    Ortí, G., Bell, M. A., Reimchen, T. E. & Meyer, A. Global survey of mitochondrial DNA sequences in the threespine sticklebacks: Evidence for recent migrations. Evolution 48, 608–622 (1994).PubMed 

    Google Scholar 
    Mäkinen, H. S. & Merilä, J. Mitochondrial DNA phylogeography of the three-spined stickleback (Gasterosteus aculeatus) in Europe: Evidence for multiple glacial refugia. Mol. Phylogenet. Evol. 46, 167–182 (2008).PubMed 

    Google Scholar 
    Thomson, R. E. Oceanography of the British Columbia Coast (Department of Fisheries and Oceans, 1981).
    Google Scholar 
    Emmett, R. et al. Geographic signatures of North American west coast estuaries. Estuaries 23, 765 (2000).CAS 

    Google Scholar 
    Dallimore, A. & Jmieff, D. Canadian west coast fjords and inlets. Geol. Soc. Spec. Pub. 344, 143–162 (2010).
    Google Scholar 
    Schoch, G. C., Albert, D. M. & Shanley, C. S. An estuarine habitat classification for a complex fjordal island archipelago. Estuaries Coasts 37, 160–176 (2014).
    Google Scholar 
    Rudnick, D. L. & Ferrari, R. Compensation of horizontal temperature and salinity gradients in the ocean mixed layer. Science 283, 526–529 (1999).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Barrett, R. D. H., Rogers, S. M. & Schluter, D. Environment specific pleiotropy facilitates divergence at the Ectodysplasin locus in threespine stickleback. Evolution 63, 2831–2837 (2009).PubMed 

    Google Scholar 
    McCairns, R. J. S. & Bernatchez, L. Plasticity and heritability of morphological variation within and between parapatric stickleback demes. J. Evol. Biol. 25, 1097–1112 (2012).CAS 
    PubMed 

    Google Scholar 
    Webster, M. M., Atton, N., Hart, P. J. B. & Ward, A. J. W. Habitat-specific morphological variation among threespine sticklebacks (Gasterosteus aculeatus) within a drainage basin. PLoS ONE 6, e21060 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Spoljaric, M. A. & Reimchen, T. E. 10 000 years later: evolution of body shape in Haida Gwaii three-spined stickleback. J. Fish. Biol. 70, 1484–1503 (2007).
    Google Scholar 
    Spoljaric, M. A. & Reimchen, T. E. Habitat-dependent reduction of sexual dimorphism in geometric body shape of Haida Gwaii threespine stickleback. Biol. J. Linn. Soc. 95, 505–516 (2008).
    Google Scholar 
    Spoljaric, M. A. & Reimchen, T. E. Habitat-specific trends in ontogeny of body shape in stickleback from coastal archipelago: Potential for rapid shifts in colonizing populations. J. Morphol. 272, 590–597 (2011).CAS 
    PubMed 

    Google Scholar 
    Morris, M. R. J. et al. Gene expression plasticity evolves in response to colonization of freshwater lakes in threespine stickleback. Mol. Ecol. 23, 3226–3240 (2014).PubMed 

    Google Scholar 
    Ramler, D., Mitteroecker, P., Shama, L. N. S., Wegner, K. M. & Ahnelt, H. Nonlinear effects of temperature on body form and developmental canalization in the threespine stickleback. J. Evol. Biol. 27, 497–507 (2014).CAS 
    PubMed 

    Google Scholar 
    Mazzarella, A. B., Voje, K. L., Hansson, T. H., Taugbøl, A. & Fischer, B. Strong and parallel salinity-induced phenotypic plasticity in one generation of threespine stickleback. J. Evol. Biol. 28, 667–677 (2015).CAS 
    PubMed 

    Google Scholar 
    Leinonen, T., Cano, J. M., Mäkinen, H. & Merilä, J. Contrasting patterns of body shape and neutral genetic divergence in marine and lake populations of threespine sticklebacks. J. Evol. Biol. 19, 1803–1812 (2006).CAS 
    PubMed 

    Google Scholar 
    Schluter, D., Marchinko, K. B., Barrett, R. D. H. & Rogers, S. M. Natural selection and the genetics of adaptation in threespine stickleback. Phil. Trans. R. Soc. B 365, 2479–2486 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    Rogers, S. M. et al. Genetic signature of adaptive peak shift in threespine stickleback. Evolution 66, 2439–2450 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Jamniczky, H. A., Barry, T. N. & Rogers, S. M. Eco-evo-devo in the study of adaptive divergence: Examples from threespine stickleback (Gasterosteus aculeatus). Integr. Comp. Biol. 55, 166–178 (2015).PubMed 

    Google Scholar 
    Gow, J. L., Rogers, S. M., Jackson, M. & Schluter, D. Ecological predictions lead to the discovery of a benthic–limnetic sympatric species pair of threespine stickleback in Little Quarry Lake, British Columbia. Can. J. Zool. 86, 564–571 (2008).
    Google Scholar 
    McPhail, J. D. Genetic evidence for a species pair in Enos Lake, British Columbia. Can. J. Zool. 62, 1402–1408 (1984).
    Google Scholar 
    McPhail, J. D. Ecology and evolution of sympatric sticklebacks (Gasterosteus): Origin of the species pairs. Can. J. Zool. 71, 515–523 (1993).
    Google Scholar 
    Kimmel, C. B., Aguirre, W., Ullmann, B., Currey, M. & Cresko, W. Allometric change accompanies opercular shape evolution in Alaskan threespine sticklebacks. Behaviour 145, 669–691 (2008).
    Google Scholar 
    Wootton, R. J. A Functional Biology of Sticklebacks (Croom Helm, 1984).
    Google Scholar 
    Kitano, J., Mori, S. & Peichel, C. L. Sexual dimorphism in the external morphology of the threespine stickleback (Gasterosteus aculeatus). Copeia 2, 336–349 (2007).
    Google Scholar 
    Aguirre, W. E., Ellis, K. E., Kusenda, M. & Bell, M. A. Phenotypic variation and sexual dimorphism in anadromous threespine stickleback: Implications for postglacial adaptive radiation. Biol. J. Linn. Soc. 95, 465–478 (2008).
    Google Scholar 
    Davenne, E. & Masson, D. Water properties in the Straits of Georgia and Juan de Fuca. 41 http://www.pac.dfo-mpo.gc.ca/sci/osap/projects/straitofgeorgia/JdFG_e.pdf (2001).Irvine, J. R. & Crawford, W. R. State of the Ocean Report for the Pacific North Coast Integrated Management Area (PNCIMA). 51 (2011).DFO. Data from British Columbia (BC) Lighthouses. Department of Fisheries and Oceans https://www.dfo-mpo.gc.ca/science/data-donnees/lightstations-phares/index-eng.html (2020).Palumbi, S. R. Genetic divergence, reproductive isolation, and marine speciation. Annu. Rev. Ecol. Evol. Syst. 25, 547–572 (1994).
    Google Scholar 
    Griffin, D. A. & LeBlond, P. H. Estuary/ocean exchange controlled by spring-neap tidal mixing. Estuar. Coast Shelf. Sci. 30, 275–297 (1990).ADS 

    Google Scholar 
    Vaz, N., Dias, J. M., Leitão, P. & Martins, I. Horizontal patterns of water temperature and salinity in an estuarine tidal channel: Ria de Aveiro. Ocean Dyn. 55, 416–429 (2005).ADS 

    Google Scholar 
    Rybkina, E. V., Ivanova, T. S., Ivanov, M. V., Kucheryavyy, A. V. & Lajus, D. L. Habitat preference of three-spined stickleback juveniles in experimental conditions and in wild eelgrass. J. Mar. Biol. Ass. UK 97, 1437–1445 (2017).
    Google Scholar 
    Flynn, S., Cadrin, C. & Filatow, D. Estuaries in British Columbia. 6 (2006).Kelly, J. R., Proctor, H. & Volpe, J. P. Intertidal community structure differs significantly between substrates dominated by native eelgrass (Zostera marina L.) and adjacent to the introduced oyster Crassostrea gigas (Thunberg) in British Columbia, Canada. Hydrobiologia 596, 57–66 (2008).
    Google Scholar 
    Fagherazzi, S. et al. Ecogeomorphology of Salt Marshes. In The Ecogeomorphology of Tidal Marshes (eds Blum, L. K. & Marani, M.) 182–200 (American Geophysical Union, 2004).
    Google Scholar 
    Campbell, A. Vegetation-environment relationships and plant community classification and ordination in British Columbia coastal salt marshes. Master’s Thesis. (University of British Columbia, 1986).Kjerfve, B. Comparative oceanography of coastal lagoons. in Estuarine Variability (ed. Wolfe, D. A.) 63–81 (Academic Press, 1986). https://doi.org/10.1016/B978-0-12-761890-6.50009-5.Barnes, R. S. K. & de Villiers, C. J. Animal abundance and food availability in coastal lagoons and intertidal marine sediments. J. Mar. Biol. Ass. UK 80, 193–202 (2000).
    Google Scholar 
    Saimoto, R. K. Life history of marine stickleback in Oyster Lagoon, British Columbia. Master’s Thesis. (University of British Columbia, 1993).King, R. W. The threespine stickleback adaptive radiation: Salinity, plasticity, and the important of ancestry. Doctoral Dissertation. (Clark University, 2016).Ahnelt, H. Imprecise naming: the anadromous and the sea spawning threespine stickleback should be discriminated by names. Biologia 73, 389–392 (2018).
    Google Scholar 
    Morris, M. R. J., Bowles, E., Allen, B. E., Jamniczky, H. A. & Rogers, S. M. Contemporary ancestor? Adaptive divergence from standing genetic variation in Pacific marine threespine stickleback. BMC Evol. Biol. 18, 113 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Kim, S.-Y., Costa, M. M., Esteve-Codina, A. & Velando, A. Transcriptional mechanisms underlying life-history responses to climate change in the three-spined stickleback. Evol. Appl. 10, 718–730 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sambrook, R. J. Interactions between threespine stickleback (Gasterosteus aculeatus linnæus) and juvenile Chinook salmon (Oncorhynchus tshawytscha Walbaum) in an estuarine marsh. Master’s Thesis. (University of British Columbia, 1990). https://doi.org/10.14288/1.0098704.Jakubavičiūtė, E., Bergström, U., Eklöf, J. S., Haenel, Q. & Bourlat, S. J. DNA metabarcoding reveals diverse diet of the three-spined stickleback in a coastal ecosystem. PLoS ONE 12, e0186929 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Kennedy, G. J. A. & Strange, C. D. The distribution of salmonids in upland streams in relation to depth and gradient. J. Fish Biol. 20, 579–591 (1982).
    Google Scholar 
    Macdonald, J. S., Birtwell, I. K. & Kruzynski, G. M. Food and habitat utilization by juvenile salmonids in the Campbell River estuary. Can. J. Fish. Aquat. Sci. 44, 1233–1246 (1987).
    Google Scholar 
    Everest, F. H. & Chapman, D. W. Habitat selection and spatial interaction by juvenile chinook salmon and steelhead trout in two Idaho streams. J. Fish. Res. Bd. Can. 29, 91–100 (2011).
    Google Scholar 
    McPhail, J. D. Speciation and the evolution of reproductive isolation in the sticklebacks (Gasterosteus) of south-western British Columbia. In The Evolutionary Biology of the Threespine Stickleback (eds Bell, M. A. & Foster, S. A.) 399–471 (Oxford University Press, 1994).
    Google Scholar 
    Kimmel, C. B. et al. Independent axes of genetic variation and parallel evolutionary divergence of opercle bone shape in threespine stickleback. Evolution 66, 419–434 (2012).PubMed 

    Google Scholar 
    Østbye, K. et al. The temporal window of ecological adaptation in postglacial lakes: A comparison of head morphology, trophic position and habitat use in Norwegian threespine stickleback populations. BMC Evol. Biol. 16, 102 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Aguirre, W. E. & Akinpelu, O. Sexual dimorphism of head morphology in three-spined stickleback Gasterosteus aculeatus. J. Fish Biol. 77, 802–821 (2010).CAS 
    PubMed 

    Google Scholar 
    Reimchen, T. E. & Nosil, P. Variable predation regimes predict the evolution of sexual dimorphism in a population of threespine stickleback. Evolution 58, 1274 (2004).CAS 
    PubMed 

    Google Scholar 
    Pistore, A. Ontogeny of population-specific phenotypic variation in the threespine stickleback. Master’s Thesis. (University of Calgary, 2018).Yurtseva, A. O. et al. Aging three-spined sticklebacks Gasterosteus aculeatus: Comparison of estimates from three structures. J. Fish Biol. 95, 802–811 (2019).PubMed 

    Google Scholar 
    Picard, P. Jr., Dodson, J. J. & FitzGerald, G. J. Habitat segregation among the age groups of Gasterosteus aculeatus (Pisces: Gasterosteidae) in the middle St. Lawrence estuary, Canada. Can. J. Zool. 68, 1202–1208 (1990).
    Google Scholar 
    Reimchen, T. E., Bergström, C. A. & Nosil, P. Natural selection and the adaptive radiation of Haida Gwaii stickleback. Evol. Ecol. Res. 15, 241–269 (2013).
    Google Scholar 
    Raeymaekers, J. A. M., Delaire, L. & Hendry, A. P. Genetically based differences in nest characteristics between lake, inlet, and hybrid threespine stickleback from the Misty system, British Columbia, Cananda. Evol. Ecol. Res. 11, 905–919 (2009).
    Google Scholar 
    Di Poi, C., Lacasse, J., Rogers, S. M. & Aubin-Horth, N. Evolution of stress reactivity in stickleback. Evol. Ecol. Res. 17, 395–405 (2016).
    Google Scholar 
    Weber, J. N., Bradburd, G. S., Stuart, Y. E., Stutz, W. E. & Bolnick, D. I. Partitioning the effects of isolation by distance, environment, and physical barriers on genomic divergence between parapatric threespine stickleback. Evolution 71, 342–356 (2017).PubMed 

    Google Scholar 
    Rohlf, F. J. Package: tpsUtil, tps file utility program. Version 1. 61. Department of Ecology and Evolution, State University of New York at Stony Brook, Stony Brook, NY. (2015).Rohlf, F. J. Package: tpsDig, digitize landmarks and outlines. Version 2. 05. Department of Ecology and Evolution, State University of New York at Stony Brook, Stony Brook, NY. (2005).Adams, D. C., Collyer, M. L. & Kaliontzopoupou, A. Geomorph: Software for geometric morphometric analysis (2020).Zelditch, M. L., Swiderski, D. L. & Sheets, H. D. Geometric Morphometrics for Biologists: A Primer (Elsevier Academic Press, 2012).MATH 

    Google Scholar 
    Galipaud, M., Gillingham, M. A. F., David, M. & Dechaume-Moncharmont, F.-X. Ecologists overestimate the importance of predictor variables in model averaging: A plea for cautious interpretations. Methods Ecol. Evol. 5, 983–991 (2014).
    Google Scholar 
    Scheipl, F., Greven, H. & Kuechenhoff, H. Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. Comput. Stat. Data Anal. 52, 3283–3299 (2008).MathSciNet 
    MATH 

    Google Scholar 
    Robinson, J. James Robinson’s functions. Version 0. 0. 0. 1. Retrieved from https://rdrr.io/github/jpwrobinson/funk/. (2019).Bartoń, K. R Package: MuMIn: Multi-model inference. Version 1. 43. 17. Retrieved from https://CRAN.R-project.org/package=MuMIn. (2020).Frank, A. Diagnosing collinearity in mixed models from lme4 R package, vif.mer function [R script]. Retrieved from https://raw.githubusercontent.com/aufrank/R-hacks/master/mer-utils.R. GitHub https://raw.githubusercontent.com/aufrank/R-hacks/master/mer-utils.R. (2011).Lakens, D. Calculating and reporting effect sizes to facilitate cumulative science: a practical primer for t-tests and ANOVAs. Front. Psychol. https://doi.org/10.3389/fpsyg.2013.00863 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. & Altman, D. G. Improving bioscience research reporting: The ARRIVE guidelines for reporting animal research. PLoS Biol. 8, e1000412 (2010).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Foundation plant species provide resilience and microclimatic heterogeneity in drylands

    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

  • in

    Dark plumes of glacial meltwater affect vertical distribution of zooplankton in the Arctic

    Meredith, M. et al. Polar regions. in IPCC Special Report on the Ocean and Cryosphere in a Changing Climate (Pörtner, H.‐O. et al. Eds.). 203–320 (2019).Nummelin, A., Ilicak, M., Li, C. & Smedsrud, L. H. Consequences of future increased Arctic runoff on Arctic Ocean stratification, circulation, and sea ice cover. J. Geophys. Res. Oceans 121, 617–637 (2016).ADS 

    Google Scholar 
    Smedsrud, L. H., Sorteberg, A. & Kloster, K. Recent and future changes of the Arctic sea-ice cover. Geophys. Res. Lett. 35, L20503 (2008).ADS 

    Google Scholar 
    Ardyna, M. & Arrigo, K. R. Phytoplankton dynamics in a changing Arctic Ocean. Nat. Clim. Change 10, 892–903. https://doi.org/10.1038/s41558-020-0905-y (2020).Article 
    ADS 
    CAS 

    Google Scholar 
    Tripathy, S. C. et al. Summer variability in bio-optical properties and phytoplankton pigment signatures in two adjacent high Arctic fjords, Svalbard. Int. J. Environ. Sci. Technol. https://doi.org/10.1007/s13762-021-03767-4 (2021).Article 

    Google Scholar 
    Sagan, S. & Darecki, M. Inherent optical properties and particulate matter distribution in summer season in waters of Hornsund and Kongsfjordenen, Spitsbergen. Oceanologia 60, 65–75 (2018).
    Google Scholar 
    Mouginot, J. et al. Forty-six years of Greenland Ice Sheet mass balance from 1972 to 2018. in Proceedings of the National Academy of Sciences of the United States of America. Vol. 116. 9239–9244. Preprint at https://doi.org/10.1073/pnas.1904242116 (2019).Rignot, E., Jacobs, S., Mouginot, J. & Scheuchl, B. Ice-shelf melting around antarctica. Science 1979(341), 266–270 (2013).ADS 

    Google Scholar 
    Konik, M., Darecki, M., Pavlov, A. K., Sagan, S. & Kowalczuk, P. Darkening of the Svalbard Fjords waters observed with satellite ocean color imagery in 1997–2019. Front. Mar. Sci. 8, 27 (2021).
    Google Scholar 
    IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (2022).Szeligowska, M. et al. The interplay between plankton and particles in the Isfjorden waters influenced by marine- and land-terminating glaciers. Sci. Total Environ. 780, 146491 (2021).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Trudnowska, E., Dąbrowska, A. M., Boehnke, R., Zajączkowski, M. & Blachowiak-Samolyk, K. Particles, protists, and zooplankton in glacier-influenced coastal svalbard waters. Estuar. Coast Shelf Sci. 242, 106842 (2020).
    Google Scholar 
    Maekakuchi, M., Matsuno, K., Yamamoto, J., Abe, Y. & Yamaguchi, A. Abundance, horizontal and vertical distribution of epipelagic ctenophores and scyphomedusae in the northern Bering Sea in summer 2017 and 2018: Quantification by underwater video imaging analysis. Deep Sea Res. 2 Top. Stud. Oceanogr. 181–182, 104818 (2020).
    Google Scholar 
    Norrbin, F., Eilertsen, H. C. & Degerlund, M. Vertical distribution of primary producers and zooplankton grazers during different phases of the Arctic spring bloom. Deep Sea Res. 2 Top. Stud. Oceanogr. 56, 1945–1958 (2009).
    Google Scholar 
    Stemmann, L. et al. Vertical distribution (0–1000 m) of macrozooplankton, estimated using the Underwater Video Profiler, in different hydrographic regimes along the northern portion of the Mid-Atlantic Ridge. Deep Sea Res. 2 Top. Stud. Oceanogr. 55, 94–105 (2008).
    Google Scholar 
    Arendt, K. E. et al. Effects of suspended sediments on copepods feeding in a glacial influenced sub-Arctic fjord. J. Plankton Res. 33, 1526–1537 (2011).CAS 

    Google Scholar 
    Arimitsu, M., Piatt, J. & Mueter, F. Influence of glacier runoff on ecosystem structure in Gulf of Alaska fjords. Mar. Ecol. Prog. Ser. 560, 19–40 (2016).ADS 

    Google Scholar 
    Renner, M., Arimitsu, M. L. & Piatt, J. F. Structure of marine predator and prey communities along environmental gradients in a glaciated fjord. Can. J. Fish. Aquat. Sci. 69, 2029–2045 (2012).
    Google Scholar 
    Lydersen, C. et al. The importance of tidewater glaciers for marine mammals and seabirds in Svalbard, Norway. J. Mar. Syst. 129, 452–471. https://doi.org/10.1016/j.jmarsys.2013.09.006 (2014).Article 

    Google Scholar 
    Falk-Petersen, S., Pavlov, V., Timofeev, S. & Sargent, J. R. Climate variability and possible effects on arctic food chains: The role of Calanus. in Arctic Alpine Ecosystems and People in a Changing Environment. 147–166. https://doi.org/10.1007/978-3-540-48514-8_9 (Springer, 2007).Stempniewicz, L. et al. Visual prey availability and distribution of foraging little auks (Alle alle) in the shelf waters of West Spitsbergen. Polar Biol. 36, 949–955 (2013).
    Google Scholar 
    CAFF. Arctic Coastal Biodiversity Monitoring Plan (CAFF Monitoring Series Report No. 29). (2019).Arendt, K. E., Nielsen, T. G., Rysgaard, S. & Tönnesson, K. Differences in plankton community structure along the Godthåbsfjord, from the Greenland Ice Sheet to offshore waters. Mar. Ecol. Prog. Ser. 401, 49–62 (2010).ADS 
    CAS 

    Google Scholar 
    Blachowiak-Samolyk, K. et al. Arctic zooplankton do not perform diel vertical migration (DVM) during periods of midnight sun. Mar. Ecol. Prog. Ser. 308, 101–116 (2006).ADS 

    Google Scholar 
    Cottier, F. R., Tarling, G. A., Wold, A. & Falk-Petersen, S. Unsynchronized and synchronized vertical migration of zooplankton in a high arctic fjord. Limnol. Oceanogr. 51, 2586–2599 (2006).ADS 

    Google Scholar 
    Hobbs, L. et al. A marine zooplankton community vertically structured by light across diel to interannual timescales. Biol Lett 17, 20200810 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Wallace, M. I. et al. Comparison of zooplankton vertical migration in an ice-free and a seasonally ice-covered Arctic fjord: An insight into the influence of sea ice cover on zooplankton behavior. Limnol. Oceanogr. 55, 831–845 (2010).ADS 

    Google Scholar 
    Bandara, K. et al. Seasonal vertical strategies in a high-Arctic coastal zooplankton community. Mar. Ecol. Prog. Ser. 555, 49–64 (2016).ADS 

    Google Scholar 
    Rabindranath, A. et al. Seasonal and diel vertical migration of zooplankton in the High Arctic during the autumn midnight sun of 2008. Mar. Biodivers. 41, 365–382 (2011).
    Google Scholar 
    Piwosz, K. et al. Comparison of productivity and phytoplankton in a warm (Kongsfjorden) and a cold (Hornsund) Spitsbergen fjord in mid-summer 2002. Polar Biol. 32, 549–559 (2009).
    Google Scholar 
    Frank, T. M. & Widder, E. A. Effects of a decrease in downwelling irradiance on the daytime vertical distribution patterns of zooplankton and micronekton. Mar. Biol. 140, 1181–1193 (2002).
    Google Scholar 
    Ortega, J. C. G., Figueiredo, B. R. S., da Graça, W. J., Agostinho, A. A. & Bini, L. M. Negative effect of turbidity on prey capture for both visual and non-visual aquatic predators. J. Anim. Ecol. 89, 2427–2439. https://doi.org/10.1111/1365-2656.13329 (2020).Article 
    PubMed 

    Google Scholar 
    Aksnes, D. et al. Coastal water darkening and implications for mesopelagic regime shifts in Norwegian fjords. Mar. Ecol. Prog. Ser. 387, 39–49 (2009).ADS 
    CAS 

    Google Scholar 
    Urbanski, J. A. et al. Subglacial discharges create fluctuating foraging hotspots for sea birds in tidewater glacier bays. Sci. Rep. 7, 1–12 (2017).
    Google Scholar 
    Weslawski, J. M., Pedersen, G., Petersen, S. F. & Porazinski, K. Entrapment of macroplankton in an Arctic fjord basin, Kongsfjorden, Svalbard. Oceanologia 42, 1 (2000).
    Google Scholar 
    Berge, J. et al. Arctic complexity: A case study on diel vertical migration of zooplankton. J. Plankton Res. 36, 1279–1297 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Darnis, G. et al. From polar night to midnight sun: Diel vertical migration, metabolism and biogeochemical role of zooplankton in a high Arctic fjord (Kongsfjorden, Svalbard). Limnol. Oceanogr. 62, 1586–1605 (2017).ADS 
    CAS 

    Google Scholar 
    Descamps, S. et al. Climate change impacts on wildlife in a High Arctic archipelago – Svalbard, Norway. Glob. Chang Biol. 23, 490–502 (2017).ADS 
    PubMed 

    Google Scholar 
    Cottier, F. R. et al. Arctic fjords: A review of the oceanographic environment and dominant physical processes. Geol. Soc. Spec. Publ. 344, 35–50 (2010).ADS 

    Google Scholar 
    Inall, M. E., Nilsen, F., Cottier, F. R. & Daae, R. Shelf/fjord exchange driven by coastal-trapped waves in the Arctic. J. Geophys. Res. Oceans 120, 8283–8303 (2015).ADS 

    Google Scholar 
    Promińska, A., Cisek, M. & Walczowski, W. Kongsfjorden and Hornsund hydrography—Comparative study based on a multiyear survey in fjords of west Spitsbergen. Oceanologia 59, 397–412 (2017).
    Google Scholar 
    Agrawal, Y. C. & Pottsmith, H. C. Instruments for particle size and settling velocity observations in sediment transport. Mar. Geol. 168, 89–114 (2000).ADS 

    Google Scholar 
    Basedow, S. L., Tande, K. S. & Zhou, M. Biovolume spectrum theories applied: Spatial patterns of trophic levels within a mesozooplankton community at the polar front. J. Plankton Res. 32, 1105–1119 (2010).PubMed 

    Google Scholar 
    Trudnowska, E., Basedow, S. L. & Blachowiak-Samolyk, K. Mid-summer mesozooplankton biomass, its size distribution, and estimated production within a glacial Arctic fjord (Hornsund, Svalbard). J. Mar. Syst. 137, 55–66 (2014).
    Google Scholar 
    Jakubas, D. et al. Foraging closer to the colony leads to faster growth in little auks. Mar. Ecol. Prog. Ser. 489, 263–278 (2013).ADS 

    Google Scholar 
    Basedow, S. L., Tande, K. S., Norrbin, M. F. & Kristiansen, S. A. Capturing quantitative zooplankton information in the sea: Performance test of laser optical plankton counter and video plankton recorder in a Calanus finmarchicus dominated summer situation. Prog. Oceanogr. 108, 72–80 (2013).ADS 

    Google Scholar 
    Woźniak, S. B., Darecki, M., Zabłocka, M., Burska, D. & Dera, J. New simple statistical formulas for estimating surface concentrations of suspended particulate matter (SPM) and particulate organic carbon (POC) from remote-sensing reflectance in the southern Baltic Sea. Oceanologia 58, 161–175 (2016).
    Google Scholar 
    Marker, A. The measurement of photosynthetic pigments in freshwaters and standardization of methods : Conclusions and recommendations. Arch. Hydrobiol. Beih 14, 91–106 (1980).CAS 

    Google Scholar 
    Stramska, M. Bio-optical relationships and ocean color algorithms for the north polar region of the Atlantic. J. Geophys. Res. 108, 3143 (2003).ADS 

    Google Scholar 
    Picheral, M. et al. The Underwater Vision Profiler 5: An advanced instrument for high spatial resolution studies of particle size spectra and zooplankton. Limnol. Oceanogr. Methods 8, 462–473 (2010).
    Google Scholar 
    Gabrielsen, T. M. et al. Potential misidentifications of two climate indicator species of the marine arctic ecosystem: Calanus glacialis and C. finmarchicus. Polar Biol. 35, 1621–1628 (2012).
    Google Scholar 
    Trudnowska, E. et al. In a comfort zone and beyond—Ecological plasticity of key marine mediators. Ecol. Evol. 10, 14067–14081 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    Jakobsson, M. et al. The International Bathymetric Chart of the Arctic Ocean version 4.0. Sci Data 7, 1–14 (2020).
    Google Scholar 
    van Rossum, G. & Drake, F. L. Python 3 Reference Manual. Preprint (2009).Caswell, T. A. et al. matplotlib/matplotlib: REL: v3.1.1. https://doi.org/10.5281/ZENODO.3264781 (2019).Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
    Google Scholar 
    Mckinney, W. Data Structures for Statistical Computing in Python. (2010).Reback, J. et al. pandas-dev/pandas: Pandas 1.0.5. https://doi.org/10.5281/ZENODO.3898987 (2020).Pond, S. & Pickard, G. L. Introductory dynamical oceanography. 2nd Ed. (1983).Mojica, K. D. A. et al. Phytoplankton community structure in relation to vertical stratification along a north-south gradient in the Northeast Atlantic Ocean. Limnol. Oceanogr. 60, 1498–1521 (2015).ADS 

    Google Scholar 
    Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA+ for PRIMER: Guide to Software and Statistical Methods. http://www.primer-e.com (2008).Clarke, K. R. & Gorley, R. N. Getting Started with PRIMER v7 Plymouth Routines in Multivariate Ecological Research. www.primer-e.com (2015).Virtanen, P. et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).Terpilowski, M. scikit-posthocs: Pairwise multiple comparison tests in Python. J. Open Source Softw. 4, 1169 (2019).ADS 

    Google Scholar 
    Alcaraz, M. et al. The role of arctic zooplankton in biogeochemical cycles: Respiration and excretion of ammonia and phosphate during summer. Polar Biol. 33, 1719–1731 (2010).
    Google Scholar 
    Soviadan, Y. D. et al. Patterns of mesozooplankton community composition and vertical fluxes in the global ocean. Prog. Oceanogr. 200, 102717 (2022).
    Google Scholar 
    Falk-Petersen, S. et al. Vertical migration in high Arctic waters during autumn 2004. Deep Sea Res. 2 Top. Stud. Oceanogr. 55, 2275–2284 (2008).
    Google Scholar 
    Lane, P. V. Z., Llinás, L., Smith, S. L. & Pilz, D. Zooplankton distribution in the western Arctic during summer 2002: Hydrographic habitats and implications for food chain dynamics. J. Mar. Syst. 70, 97–133 (2008).
    Google Scholar 
    Kulk, G., Poll, W. H. & Buma, A. G. J. Photophysiology of nitrate limited phytoplankton communities in Kongsfjorden, Spitsbergen. Limnol. Oceanogr. 63, 2606–2617 (2018).ADS 
    CAS 

    Google Scholar 
    Moskalik, M. et al. Spatiotemporal changes in the concentration and composition of suspended particulate matter in front of Hansbreen, a tidewater glacier in Svalbard. Oceanologia 60, 446–463 (2018).
    Google Scholar 
    Svendsen, H. et al. The physical environment of Kongsfjorden-Krossfjorden, an Arctic fjord system in Svalbard. Polar Res. 21, 133–166 (2002).
    Google Scholar 
    Chiswell, S. M., Calil, P. H. R. & Boyd, P. W. Spring blooms and annual cycles of phytoplankton: A unified perspective. J. Plankton Res. 37, 500–508 (2015).
    Google Scholar 
    Kaartvedt, S., Melle, W., Knutsen, T. & Skjoldal, H. Vertical distribution of fish and krill beneath water of varying optical properties. Mar. Ecol. Prog. Ser. 136, 51–58 (1996).ADS 

    Google Scholar 
    Schmid, M. S., Maps, F. & Fortier, L. Lipid load triggers migration to diapause in Arctic Calanus copepods—Insights from underwater imaging. J. Plankton Res. 40, 311–325 (2018).CAS 

    Google Scholar 
    Campbell, R. G. et al. Mesozooplankton prey preference and grazing impact in the western Arctic Ocean. Deep Sea Res. 2 Top. Stud. Oceanogr. 56, 1274–1289 (2009).
    Google Scholar 
    Hirche, H. J. Diapause in the marine copepod, calanus finmarchicus—A review. Ophelia 44, 129–143 (1996).
    Google Scholar 
    Pedersen, S. A. & Smidt, E. L. B. Zooplankton Investigations Off West Greenland, 1956–1984. (ICES, 1995).Reiner Vonnahme, T. et al. Early spring subglacial discharge plumes fuel under-ice primary production at a Svalbard tidewater glacier. Cryosphere 15, 2083–2107 (2021).ADS 

    Google Scholar 
    Majaneva, S. et al. Aggregations of predators and prey affect predation impact of the Arctic ctenophore Mertensia ovum. Mar. Ecol. Prog. Ser. 476, 87–100 (2013).ADS 

    Google Scholar 
    Purcell, J. E., Hopcroft, R. R., Kosobokova, K. N. & Whitledge, T. E. Distribution, abundance, and predation effects of epipelagic ctenophores and jellyfish in the western Arctic Ocean. Deep Sea Res. 2 Top Stud Oceanogr 57, 127–135 (2010).
    Google Scholar 
    Condon, R. H. et al. Questioning the rise of gelatinous zooplankton in the world’s oceans. Bioscience 62, 160–169 (2012).
    Google Scholar 
    Balazy, K., Trudnowska, E. & Błachowiak-Samołyk, K. Dynamics of Calanus copepodite structure during little Auks’ breeding seasons in two different Svalbard locations. Water (Basel) 11, 1405 (2019).CAS 

    Google Scholar 
    Karnovsky, N. J. & Hunt, G. L. Estimation of carbon flux to dovekies (Alle alle) in the North Water. Deep Sea Res. 2 Top. Stud. Oceanogr. 49, 5117–5130 (2002).CAS 

    Google Scholar 
    Renaud, P. E. et al. Is the poleward expansion by Atlantic cod and haddock threatening native polar cod, Boreogadus saida?. Polar Biol. 35, 401–412. https://doi.org/10.1007/s00300-011-1085-z (2012).Article 

    Google Scholar 
    Szeligowska, M. et al. Spatial patterns of particles and plankton in the warming Arctic Fjord (Isfjorden, West Spitsbergen) in seven consecutive mid-summers (2013–2019). Front. Mar. Sci. 7, 584 (2020).
    Google Scholar  More

  • in

    Subalpine woody vegetation in the Eastern Carpathians after release from agropastoral pressure

    Bolliger, J., Kienast, F. & Zimmermann, N. E. Risk of global warming on montane and subalpine forests in Switzerland—A modeling study. Reg. Environ. Change 1, 99–111 (2000).
    Google Scholar 
    Bugmann, H. & Pfister, Ch. Impacts of interannual climate variability on past and future forest composition. Reg. Environ. Change 1, 112–125 (2000).
    Google Scholar 
    Becker, A. & Bugmann, H. (eds.) Global change and mountain regions: The Mountain Research Initiative. IHDP Report 13, GTOS Report 28 and IGBP Report 49, Stockholm (2001).Kullman, L. 20th Century climate warming and tree-limit rise in the southern Scandes of Sweden. Ambio 30, 72–80. https://doi.org/10.1579/0044-7447-30.2.72 (2001).CAS 
    PubMed 

    Google Scholar 
    Körner, Ch. & Paulsen, J. A world-wide study of high altitude treeline temperatures. J. Biogeogr. 31, 713–732. https://doi.org/10.1111/j.1365-2699.2003.01043.x (2004).
    Google Scholar 
    Harsch, M. A. & Bader, M. Y. Treeline form—A potential key to understanding treeline dynamics. Global Ecol. Biogeogr. 20, 582–596. https://doi.org/10.1111/j.1466-8238.2010.00622.x (2011).
    Google Scholar 
    Tokarczyk, N. Forest encroachment on temperate mountain meadows: scale, drivers, and current research directions. Geogr. Pol. 90, 463–480 (2017).
    Google Scholar 
    Vitali, A. et al. Pine recolonization dynamics in Mediterranean human-disturbed treeline ecotones. For. Ecol. Manag. 435, 28–37. https://doi.org/10.1016/j.foreco.2018.12.039 (2019).
    Google Scholar 
    Heikkinen, O., Obrębska-Starkel, B. & Tuhkanen, S. Introduction: the timberline—A changing battlefront. Prace Geograficzne UJ 98, 7–16 (1995).
    Google Scholar 
    Mattson, J. Human impact on the timberline in the far North of Europe. Zeszyty Naukowe UJ, Prace Geogr. 98, 41–56 (1995).
    Google Scholar 
    Stanisci, A., Lavieri, D., Acosta, A. & Blasi, C. Structure and diversity trends at Fagus timberline in central Italy. Community Ecol. 1, 133–138 (2000).
    Google Scholar 
    Gehrig-Fasel, J., Guisan, A. & Zimmermann, N. E. Tree line shifts in the Swiss Alps: Climate change or land abandonment?. J. Veg. Sci. 18, 571–582 (2007).
    Google Scholar 
    Feurdean, A. et al. Long-term land-cover/use change in a traditional farming landscape in Romania inferred from pollen data, historical maps and satellite images. Reg. Environ. Change 17, 2193–2207. https://doi.org/10.1007/s10113-016-1063-7 (2017).
    Google Scholar 
    Burga, C. A., Bührer, S. & Klötzli, F. Mountain ash (Sorbus aucuparia) forests of the Central and Southern Alps (Grisons and Ticino, Switzerland-Prov. Verbano-Cusio-Ossola, N-Italy): Plant ecological and phytosociological aspects. Tuexenia 39, 121–138 (2019).
    Google Scholar 
    Slayter, R. O. & Noble, I. R. Dynamics of Montane Treelines. In Landscape Boundaries, Consequences for Biotic Diversity and Ecological Flows. Ecological Studies Vol. 92 (eds Hansen, A. J. & di Castri, F.) 346–359 (Springer-Verlag, 1992).
    Google Scholar 
    Bryn, A. Recent forest limit changes in south-east Norway: Effects of climate change or regrowth after abandoned utilisation?. Nor. Geogr. Tidsskr. 62(4), 251–270. https://doi.org/10.1080/00291950802517551 (2008).
    Google Scholar 
    Lu, X., Liang, E., Wang, Y., Babst, F. & Camarero, J. J. Mountain treelines climb slowly despite rapid climate warming. Glob. Ecol. Biogeogr. 30(1), 305–315. https://doi.org/10.1111/geb.13214 (2021).
    Google Scholar 
    Armand, A. D. Sharp and Gradual Mountain Timberlines as Result of species Interaction. Landscape Boundaries, Consequences for Biotic Diversity and Ecological Flows. In Ecological Studies Vol. 92 (eds Hansen, A. J. & di Castri, F.) 360–377 (Springer-Verlag, 1992).
    Google Scholar 
    Kucharzyk, S. Ekologiczne znaczenie drzewostanów w strefie górnej granicy lasu w Karpatach Wschodnich i ich wrażliwość na zmiany antropogeniczne [Ecological importance of stands at the upper forest limit in the Eastern Carpathians and their sensibility to anthropogenic changes]. Roczn. Bieszcz. 14, 15–43 (2006) (in Polish with English summary).
    Google Scholar 
    Surina, B. & Rakaj, M. Subalpine beech forest with Hairy alpenrose (Polysticho lonchitis-Fagetum Rhododendretosum hirsuti subass. nova) on Mt. Snežnik (Liburnian Karst, Dinaric Mts). Hacquetia 6, 195–208 (2007).
    Google Scholar 
    Kucharzyk, S. Zmiany przebiegu górnej granicy lasu w pasmie Szerokiego Wierchu w Bieszczadzkim Parku Narodowym [Changes of upper forest limit in the Szeroki Wierch range (Bieszczady National Park)]. Roczn. Bieszcz. 12, 81–102 (2004) (in Polish with English summary).
    Google Scholar 
    Kucharzyk, S. & Augustyn, M. Dynamika górnej granicy lasu w Bieszczadach Zachodnich – zmiany w ciągu półtora wieku [The upper forest limit dynamics in the Western Bieszczady Mts.—Changes over a century and a half]. Stud. Nat. 54, 133–156 (2008) (in Polish with English summary).
    Google Scholar 
    Kubijowicz, W. Życie pasterskie w Beskidach Wschodnich [La Vie Pastorale dans les Beskides Orientales]. Prace Instytutu Geograficznego UJ 5, 3–30 (1926) (in Polish).
    Google Scholar 
    Zarzycki, K. Lasy Bieszczadów Zachodnich [The forests of the Western Bieszczady Mts (Polish Eastern Carpathians)]. Acta Agr. et Silv. Ser. Leśna 3, 1–131 (1963) (in Polish with English summary).
    Google Scholar 
    Augustyn, M. Połoniny w Bieszczadach Zachodnich [Almen im westlichen Bieszczady-Gebirge]. Materiały Muzeum Budownictwa Ludowego w Sanoku 31, 88–98 (1993) (in Polish with German summary).
    Google Scholar 
    Winnicki, T. Zbiorowiska roślinne połonin Bieszczadzkiego Parku Narodowego (Bieszczady Zachodnie, Karpaty Wschodnie) [Plant communities of subalpine poloninas in the Bieszczady National Park (Western Bieszczady Mts, Eastern Carpathians)]. Monogr. Bieszczadzkie 4, 1–215 (1999) (in Polish with English summary).
    Google Scholar 
    Mróz, W. Zróżnicowanie szaty roślinnej przy górnej granicy lasu w Bieszczadach Wschodnich i Zachodnich [The diversity of vegetation near the upper timberline in the Eastern and the Western Bieszczady Mts]. Roczn. Bieszcz. 14, 45–62 (2006) (in Polish with English summary).
    Google Scholar 
    Augustyn, M. & Kucharzyk, S. Górna granica lasu na terenie wsi Ustrzyki Górne i Wołosate w końcu XVIII wieku [Timberline in the Western Bieszczady Mts.]. Roczn. Bieszcz. 20, 15–27 (2012) (in Polish with English summary).
    Google Scholar 
    Jeník, J. Succession on the Połonina Balds in the Western Bieszczady, the Eastern Carpathians. Tuexenia 3, 207–216 (1983).
    Google Scholar 
    Michalik, S. & Szary, A. Zbiorowiska leśne Bieszczadzkiego Parku Narodowego [The forest communities of the Bieszczady National Park]. Monogr. Bieszcz. 1, 1–175 (1997).
    Google Scholar 
    Zemanek, B. & Winnicki, T. Rośliny naczyniowe Bieszczadzkiego Parku Narodowego [Vascular plants of the Bieszczady National Park]. Monogr. Bieszcz. 3, 1–249 (1999) (in Polish with English summary).
    Google Scholar 
    Kucharzyk, S. & Augustyn, M. Trwałość polan reglowych w Bieszczadzkim Parku Narodowym [Stability of mountain glades in the Bieszczady National Park]. Roczn. Bieszcz. 18, 45–58 (2010) (in Polish with English summary).
    Google Scholar 
    Durak, T., Żywiec, M. & Ortyl, B. Rozprzestrzenianie się zarośli drzewiastych w piętrze połonin Bieszczad Zachodnich [Expansion of brushwood in the subalpine zone of the Western Bieszczady Mts]. Sylwan 157, 130–138 (2013) (in Polish with English summary).
    Google Scholar 
    Durak, T., Żywiec, M., Kapusta, P. & Holeksa, J. Impact of land use and climate changes on expansion of woody species on subalpine meadows in the Eastern Carpathians. For. Ecol. Manag. 339, 127–135. https://doi.org/10.1016/j.foreco.2014.12.014 (2015).
    Google Scholar 
    Durak, T., Żywiec, M., Kapusta, P. & Holeksa, J. Rapid spread of a fleshy-fruited species in abandoned subalpine meadows—Formation of an unusual forest belt in the eastern Carpathians. iForest – Biogeosci. For. 9, 337–343. https://doi.org/10.3832/ifor1470-008 (2015).
    Google Scholar 
    Wężyk, P. & Hawryło, P. Analiza struktury 3D drzewostanów Bieszczadzkiego PN na podstawie danych lotniczego skanowania laserowego oraz ortofotomap lotniczych CIR [3D structure analysis of stands of the Bieszczady National Park on the basis of airborne laser scanning data and CIR aerial ortho-photomaps] (ProGea Consulting, 2015) (in Polish).Anselin, L. Local indicators of spatial association—LISA. Geogr. Anal. 27, 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x (1995).
    Google Scholar 
    Scott, L. M. & Janikas, M. V. Spatial Statistics in ArcGIS. In Handbook of Applied Spatial Analysis (eds Fischer, M. M. & Getis, A.) 27–41 (Springer, 2010).
    Google Scholar 
    Cui, H., Wu, L., Hu, S., Lu, R. & Wang, S. Research on the driving forces of urban hot spots based on exploratory analysis and binary logistic regression model. Trans. GIS 25(3), 1522–1541. https://doi.org/10.1111/tgis.12739 (2021).
    Google Scholar 
    Pierce, K. B., Lookingbill, T. & Urban, D. A simple method for estimating potential relative radiation (PRR) for landscape-scale vegetation analysis. Landsc. Ecol. 20, 137–147 (2005).
    Google Scholar 
    Riley, S. J., DeGloria, S. D. & Elliot, R. A terrain ruggedness index that quantifies topographic heterogeneity. Int. J. Sc. 5, 23–27 (1999).
    Google Scholar 
    Böhner, J. & Antonić, O. Land-surface parameters specific to topo-climatology. Geomorphometry – Concepts, Softw. Appl. Dev. Soil Sci. 33, 195–226. https://doi.org/10.1016/S0166-2481(08)00008-1 (2009).
    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2021).
    Google Scholar 
    Agresti, A. An Introduction to Categorical Data Analysis 2nd edn. (Wiley & Sons Inc., 2007).MATH 

    Google Scholar 
    Cottrell, A. Gnu Regression, Econometrics and Time-series Library gretl. http://gretl.sourceforge.net/(2020).Hellevik, O. Linear versus logistic regression when the dependent variable is a dichotomy. Qual. Quant. 43, 59–74 (2009).
    Google Scholar 
    Azen, R. & Traxel, N. Using dominance analysis to determine predictor importance in logistic regression. J. Educ. Behav. Stat. 34, 319–347. https://doi.org/10.3102/1076998609332754 (2009).
    Google Scholar 
    Borcard, P., Legendre, P. & Drapeau, P. Partialling out the spatial component of ecological variation. Ecology 73, 1045–1055 (1992).
    Google Scholar 
    Przybylska, K. & Kucharzyk, S. Skład gatunkowy i struktura lasów Bieszczadzkiego Parku Narodowego [Species composition and structure of forest of the Bieszczady National Park. Monogr. Bieszcz. 6, 1–159 (1999) (in Polish with English summary).
    Google Scholar 
    Bader, M. Y. et al. A global framework for linking alpine-treeline ecotone patterns to underlying processes. Ecography 44(2), 265–292. https://doi.org/10.1111/ecog.05285 (2021).
    Google Scholar 
    Nowosad, M. Zarys klimatu Bieszczadzkiego Parku Narodowego i jego otuliny w świetle dotychczasowych badań [Outlines of climate of the Bieszczady National Park and its bufferzone in the light of previous studies]. Roczn. Bieszcz. 4, 163–183 (1995) (in Polish with English summary).
    Google Scholar 
    Nowosad, M. & Wereski, S. Warunki klimatyczne. Bieszczadzki Park Narodowy–40 lat ochrony [Climatic conditions. Bieszczady National Park–40 years of protection]. In Bieszczadzki Park Narodowy [The Bieszczady National Park] (eds Górecki, A. & Zemanek, B.) 31–38 (Wyd. Bieszczadzki Park Narodowy, 2016) (in Polish with English summary).
    Google Scholar 
    Kukulak, J. Neotectonics and planation surfaces in the High Bieszczady Mountains (Outer Carpathians, Poland). Ann. Soc. Geol. Pol. 74, 339–350 (2004).
    Google Scholar 
    Haczewski, G., Kukulak, J. & Bąk, K. Budowa geologiczna i rzeźba Bieszczadzkiego Parku Narodowego [Geology and relief of the Bieszczady National Park]. Prace monograficzne (Akademia Pedagogiczna im. Komisji Edukacji Narodowej w Krakowie) 468, 1–156 (2007) (in Polish with English summary).
    Google Scholar 
    Skiba, S., Drewnik, M., Kacprzak, A. & Kołodziejczyk, M. Gleby litogeniczne Bieszczadów i Beskidu Niskiego [Lithogenous soils of the Bieszczady and Beskid Niski Mts (Polish Carpathians)]. Roczn. Bieszcz. 7, 387–396 (1998) (in Polish with English summary).
    Google Scholar 
    Skiba, S. & Winnicki, T. Gleby zbiorowisk roślinnych bieszczadzkich połonin [Soils of the subalpine meadows plant communities in the Bieszczady Mts]. Roczn. Bieszcz. 4, 97–109 (1995) (in Polish with English summary).
    Google Scholar 
    Musielok, Ł, Drewnik, M., Szymański, W. & Stolarczyk, M. Classification of mountain soils in a subalpine zone—A case study from the Bieszczady Mountains (SE Poland). Soil Sci. Annu. 70, 170–177. https://doi.org/10.2478/ssa-2019-0015 (2019).CAS 

    Google Scholar 
    Spatz, G. Succession patterns on mountain pastures. Vegetatio 43, 39–41 (1980).
    Google Scholar 
    Kozak, J. Zmiany powierzchni lasów w Karpatach Polskich na tle innych gór świata [Changes in the Land Cover in the Polish Carpathians at the Turn of the 20th and 21st Century in Relation to Local Development Level]. Wydawnictwo Uniwersytetu Jagiellońskiego, Kraków (2005) (in Polish with English summary).Vitali, A., Urbinati, C., Weisberg, P. J., Urza, A. K. & Garbarino, M. Effects of natural and anthropogenic drivers on land-cover change and treeline dynamics in the Apennines (Italy). J. Veg. Sci. 29(2), 189–199. https://doi.org/10.1111/jvs.12598 (2018).
    Google Scholar 
    Micu, D. M., Dumitrescu, A., Cheval, S., Nita, I.-A. & Birsan, M.-V. Temperature changes and elevation-warming relationships in the Carpathian Mountains. Int. J. Climatol. 41, 2154–2172. https://doi.org/10.1002/joc.6952 (2020).
    Google Scholar 
    Rehman, A. Ziemie dawnej Polski. Cz. I. Karpaty [The lands of ancient Poland. Part I. The Carpathians]. (Gubrynowicz i Schmidt, Lwów) (1895) (in Polish).Frey, W. The influence of snow on growth and survival of planted trees. Arct. Alp. Res. 15, 241–251 (1983).
    Google Scholar 
    Malanson, G. P. et al. Alpine treeline of Western North America: Linking organism-to-landscape dynamics. Phys. Geogr. 28, 378–396. https://doi.org/10.2747/0272-3646.28.5.378 (2007).
    Google Scholar 
    Holtmeier, F. K. & Broll, G. Wind as an ecological agent at treelines in North America, the Alps, and the European Subarctic. Phys. Geogr. 31, 203–233. https://doi.org/10.2747/0272-3646.31.3.203 (2010).
    Google Scholar 
    Barclay, A. M. & Crawford, R. M. M. Winter desiccation stress and resting bud viability in relation to high altitude survival in Sorbus aucuparia L. Flora 172, 21–34 (1982).
    Google Scholar 
    Raspé, O., Findlay, C. & Jacquemart, A. L. Sorbus aucuparia L. J. Ecol. 88, 910–930 (2000).
    Google Scholar 
    Zerbe, S. On the ecology of Sorbus aucuparia (Rosaceae) with special regard to germination, establishment and growth. Pol. Bot. J. 46, 229–239 (2001).
    Google Scholar 
    Smith, W. K., Germino, M. J., Hancock, T. E. & Johnson, D. M. Another perspective on altitudinal limits of alpine timberlines. Tree Physiol. 23, 1101–1112 (2003).PubMed 

    Google Scholar 
    Trant, A., Higgs, E. & Starzomski, B. M. A century of high elevation ecosystem change in the Canadian Rocky Mountains. Sci. Rep. 10, 9698. https://doi.org/10.1038/s41598-020-66277-2 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Barbeito, I., Dawes, M. A., Rixen, C., Senn, J. & Bebi, P. Factors driving mortality and growth at treeline: A 30-year experiment of 92 000 conifers. Ecology 93(2), 389–401 (2012).PubMed 

    Google Scholar 
    Kullman, L. A 25-year survey of geoecological change in the scandes mountains of Sweden. Geogr. Ann. Ser. B 79, 139–165 (1997).
    Google Scholar 
    Pękala, K. Rzeźba Bieszczadzkiego Parku Narodowego [Relief of the Bieszczady National Park]. Roczn. Bieszcz. 6, 19–38 (1997) (in Polish with English summary).
    Google Scholar 
    Kullman, L. Temporal and spatial aspects of subalpine populations of Sorbus aucuparia in Sweden. Ann. Bot. Fenn. 23, 267–275 (1986).
    Google Scholar 
    Hoersch, B. Modelling the spatial distribution of montane and subalpine forests in the Central Alps using digital elevation models. Ecol. Model. 168, 267–282 (2003).
    Google Scholar 
    Resler, L. M., Butler, D. R. & Malanson, G. P. Topographic shelter and conifer establishment and mortality in an alpine environment, Glacier National Park, Montana. Phys. Geogr. 26, 112–125 (2005).
    Google Scholar 
    Kollmann, J. Regeneration window for fleshy-fruited plants during scrub development on abandoned grassland. Ecoscience 2, 213–222 (1995).
    Google Scholar 
    Lediuk, K. D., Damascos, M. A., Puntieri, J. G. & de Torres Curth, M. I. Population dynamics of an invasive tree, Sorbus aucuparia, in the understory of a Patagonian forest. Plant Ecol. 217, 899–911 (2016).
    Google Scholar 
    McCutchan, M. H. & Fox, D. G. Effect of elevation and aspect on wind, temperature and humidity. J. Appl. Meteorol. Climatol. 25(12), 1996–2013 (1986).ADS 

    Google Scholar 
    Stage, A. R. & Salas, C. Interactions of elevation, aspect, and slope in models of forest species composition and productivity. For. Sci. 53, 486–492 (2007).
    Google Scholar 
    Pocewicz, A. L., Gessler, P. & Robinson, A. P. The relationship between effective plant area index and Landsat spectral response across elevation, solar insolation, and spatial scales in a northern Idaho forest. Can. J. For. Res. 34, 465–480 (2004).
    Google Scholar 
    Kucharzyk, S. & Sugiero, D. Zróżnicowanie dynamiki procesów lasotwórczych w buczynach bieszczadzkich w zależności od wystawy i wzniesienia [Variability of the dynamics of forest development processes in the Bieszczady beech forests in relation to exposition and altitude]. Sylwan 7, 29–38 (2007) (in Polish with English summary).
    Google Scholar 
    Drewnik, M., Musielok, Ł, Stolarczyk, M., Mitka, J. & Gus, M. Effects of exposure and vegetation type on organic matter stock in the soils of subalpine meadows in the Eastern Carpathians. CATENA 147, 167–176. https://doi.org/10.1016/j.catena.2016.07.014 (2016).CAS 

    Google Scholar 
    Zheng, L. et al. Tree regeneration patterns on contrasting slopes at treeline ecotones in Eastern Tibet. Forests 12, 1605. https://doi.org/10.3390/f12111605 (2021).
    Google Scholar  More

  • in

    Contrasting response of fungal versus bacterial residue accumulation within soil aggregates to long-term fertilization

    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