More stories

  • in

    Abiotic conditions shape spatial and temporal morphological variation in North American birds

    Dehling, D. M., Jordano, P., Schaefer, H. M., Böhning-Gaese, K. & Schleuning, M. Morphology predicts species’ functional roles and their degree of specialization in plant–frugivore interactions. Proc. R. Soc. B 283, 20152444 (2016).PubMed 
    PubMed Central 

    Google Scholar 
    Grant, P. R. Inheritance of size and shape in a population of Darwin’s finches, Geospiza conirostris. Proc. R. Soc. Lond. B 220, 219–236 (1983).
    Google Scholar 
    Des Roches, S. et al. The ecological importance of intraspecific variation. Nat. Ecol. Evol. 2, 57–64 (2018).PubMed 

    Google Scholar 
    Bergmann, C. Über die verhältnisse der wärmeökonomie der thiere zu ihrer grösse. Gött. Stud. 3, 595–708 (1847).
    Google Scholar 
    Allen, J. A. The influence of physical conditions in the genesis of species. Radic. Rev. 1, 108–140 (1877).
    Google Scholar 
    Altshuler, D. L. & Dudley, R. The physiology and biomechanics of avian flight at high altitude. Integr. Comp. Biol. 46, 62–71 (2006).PubMed 

    Google Scholar 
    Teplitsky, C. & Millien, V. Climate warming and Bergmann’s rule through time: is there any evidence? Evol. Appl. 7, 156–168 (2014).PubMed 

    Google Scholar 
    Gardner, J. L., Peters, A., Kearney, M. R., Joseph, L. & Heinsohn, R. Declining body size: a third universal response to warming? Trends Ecol. Evol. 26, 285–291 (2011).PubMed 

    Google Scholar 
    Yom-Tov, Y., Yom-Tov, S., Wright, J., Thorne, C. J. R. & Du Feu, R. Recent changes in body weight and wing length among some British passerine birds. Oikos 112, 91–101 (2006).
    Google Scholar 
    Van Buskirk, J., Mulvihill, R. S. & Leberman, R. C. Declining body sizes in North American birds associated with climate change. Oikos 119, 1047–1055 (2010).
    Google Scholar 
    Weeks, B. C. et al. Shared morphological consequences of global warming in North American migratory birds. Ecol. Lett. 23, 316–325 (2020).PubMed 

    Google Scholar 
    Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).CAS 
    PubMed 

    Google Scholar 
    DeSante, D. F., Saracco, J. F., O’Grady, D. R., Burton, K. M. & Walker, B. L. Methodological considerations of the Monitoring Avian Productivity and Survivorship (MAPS) program. Stud. Avian Biol. 29, 28–45 (2004).West, G. B., Brown, J. H. & Enquist, B. J. A general model for the origin of allometric scaling laws in biology. Science 276, 122–126 (1997).CAS 
    PubMed 

    Google Scholar 
    Jirinec, V. et al. Morphological consequences of climate change for resident birds in intact Amazonian rainforest. Sci. Adv. 7, eabk1743 (2021).PubMed 
    PubMed Central 

    Google Scholar 
    Dubiner, S. & Meiri, S. Widespread recent changes in morphology of Old World birds, global warming the immediate suspect. Glob. Ecol. Biogeogr. 31, 791–801 (2022).
    Google Scholar 
    Ballinger, M. A. & Nachman, M. W. The contribution of genetic and environmental effects to Bergmann’s rule and Allen’s rule in house mice. Am. Nat. https://doi.org/10.1086/719028 (2022).Andrew, S. C., Hurley, L. L., Mariette, M. M. & Griffith, S. C. Higher temperatures during development reduce body size in the zebra finch in the laboratory and in the wild. J. Evol. Biol. 30, 2156–2164 (2017).CAS 
    PubMed 

    Google Scholar 
    Siepielski, A. M. et al. No evidence that warmer temperatures are associated with selection for smaller body sizes. Proc. R. Soc. B 286, 20191332 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Salewski, V., Siebenrock, K.-H., Hochachka, W. M., Woog, F. & Fiedler, W. Morphological change to birds over 120 years is not explained by thermal adaptation to climate change. PLoS ONE 9, e101927 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    Riddell, E. A., Iknayan, K. J., Wolf, B. O., Sinervo, B. & Beissinger, S. R. Cooling requirements fueled the collapse of a desert bird community from climate change. Proc. Natl Acad. Sci. USA 116, 21609–21615 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).PubMed 

    Google Scholar 
    Futuyma, D. J. Evolutionary constraint and ecological consequences. Evolution 64, 1865–1884 (2010).PubMed 

    Google Scholar 
    Murren, C. J. et al. Constraints on the evolution of phenotypic plasticity: limits and costs of phenotype and plasticity. Heredity 115, 293–301 (2015).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rollinson, C. R. et al. Working across space and time: nonstationarity in ecological research and application. Front. Ecol. Environ. 19, 66–72 (2021).
    Google Scholar 
    Riemer, K., Guralnick, R. P. & White, E. P. No general relationship between mass and temperature in endothermic species. eLife 7, e27166 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    Ryding, S., Klaassen, M., Tattersall, G. J., Gardner, J. L. & Symonds, M. R. Shape-shifting: changing animal morphologies as a response to climatic warming. Trends Ecol. Evol. 36, 1036–1048 (2021).PubMed 

    Google Scholar 
    Baldwin, M. W., Winkler, H., Organ, C. L. & Helm, B. Wing pointedness associated with migratory distance in common-garden and comparative studies of stonechats (Saxicola torquata). J. Evol. Biol. 23, 1050–1063 (2010).CAS 
    PubMed 

    Google Scholar 
    Förschler, M. I. & Bairlein, F. Morphological shifts of the external flight apparatus across the range of a passerine (Northern Wheatear) with diverging migratory behaviour. PLoS ONE 6, e18732 (2011).PubMed 
    PubMed Central 

    Google Scholar 
    Macpherson, M. P., Jahn, A. E. & Mason, N. A. Morphology of migration: associations between wing shape, bill morphology and migration in kingbirds (Tyrannus). Biol. J. Linn. Soc. 135, 71–83 (2022).
    Google Scholar 
    Newton, I. The Migration Ecology of Birds (Elsevier, 2010).Clegg, S. M., Kelly, J. F., Kimura, M. & Smith, T. B. Combining genetic markers and stable isotopes to reveal population connectivity and migration patterns in a neotropical migrant, Wilson’s warbler (Wilsonia pusilla). Mol. Ecol. 12, 819–830 (2003).CAS 
    PubMed 

    Google Scholar 
    Bell, C. P. Leap-frog migration in the fox sparrow: minimizing the cost of spring migration. Condor 99, 470–477 (1997).
    Google Scholar 
    Billerman, S., Keeney, B., Rodewald, P. & Schulenberg, T. (eds) Birds of the World (Cornell Laboratory of Ornithology, 2020).Desrochers, A. Morphological response of songbirds to 100 years of landscape change in North America. Ecology 91, 1577–1582 (2010).CAS 
    PubMed 

    Google Scholar 
    Swaddle, J. P. & Lockwood, R. Morphological adaptations to predation risk in passerines. J. Avian Biol. 29, 172–176 (1998).
    Google Scholar 
    Chown, S. L. & Klok, C. J. Altitudinal body size clines: latitudinal effects associated with changing seasonality. Ecography 26, 445–455 (2003).
    Google Scholar 
    Hsiung, A. C., Boyle, W. A., Cooper, R. J. & Chandler, R. B. Altitudinal migration: ecological drivers, knowledge gaps, and conservation implications: animal altitudinal migration review. Biol. Rev. 93, 2049–2070 (2018).PubMed 

    Google Scholar 
    Barras, A. G., Liechti, F. & Arlettaz, R. Seasonal and daily movement patterns of an alpine passerine suggest high flexibility in relation to environmental conditions. J. Avian Biol. 52, jav.02860 (2021).
    Google Scholar 
    Spence, A. R. & Tingley, M. W. Body size and environment influence both intraspecific and interspecific variation in daily torpor use across hummingbirds. Funct. Ecol. 35, 870–883 (2021).CAS 

    Google Scholar 
    Moreau, R. E. Variation in the western Zosteropidae (Aves). Bull. Br. Mus. Nat. Hist. Zool. 4, 311–433 (1957).
    Google Scholar 
    Hamilton, T. H. The adaptive significances of intraspecific trends of variation in wing length and body size among bird species. Evolution 15, 180–194 (1961).
    Google Scholar 
    Hodkinson, I. D. Terrestrial insects along elevation gradients: species and community responses to altitude. Biol. Rev. 80, 489–513 (2005).PubMed 

    Google Scholar 
    Feinsinger, P., Colwell, R. K., Terborgh, J. & Chaplin, S. B. Elevation and the morphology, flight energetics, and foraging ecology of tropical hummingbirds. Am. Nat. 113, 481–497 (1979).
    Google Scholar 
    Aldrich, J. W. Ecogeographical Variation in Size and Proportions of Song Sparrows (Melospiza melodia) (American Ornithological Society, 1984).Sun, Y. et al. The role of climate factors in geographic variation in body mass and wing length in a passerine bird. Avian Res. 8, 1 (2017).Des Roches, S., Pendleton, L. H., Shapiro, B. & Palkovacs, E. P. Conserving intraspecific variation for nature’s contributions to people. Nat. Ecol. Evol. 5, 574–582 (2021).PubMed 

    Google Scholar 
    McKechnie, A. E. & Wolf, B. O. Climate change increases the likelihood of catastrophic avian mortality events during extreme heat waves. Biol. Lett. 6, 253–256 (2010).PubMed 

    Google Scholar 
    Conradie, S. R., Woodborne, S. M., Cunningham, S. J. & McKechnie, A. E. Chronic, sublethal effects of high temperatures will cause severe declines in southern African arid-zone birds during the 21st century. Proc. Natl Acad. Sci. USA 116, 14065–14070 (2019).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Radchuk, V. et al. Adaptive responses of animals to climate change are most likely insufficient. Nat. Commun. 10, 3109 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    Riddell, E. A. et al. Exposure to climate change drives stability or collapse of desert mammal and bird communities. Science 371, 633–636 (2021).CAS 
    PubMed 

    Google Scholar 
    Tingley, M. W., Monahan, W. B., Beissinger, S. R. & Moritz, C. Birds track their Grinnellian niche through a century of climate change. Proc. Natl Acad. Sci. USA 106, 19637–19643 (2009).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Youngflesh, C. et al. Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up. Nat. Ecol. Evol. 5, 987–994 (2021).PubMed 

    Google Scholar 
    Blueweiss, L. et al. Relationships between body size and some life history parameters. Oecologia 37, 257–272 (1978).CAS 
    PubMed 

    Google Scholar 
    Kleiber, M. Body size and metabolic rate. Physiol. Rev. 27, 511–541 (1947).CAS 
    PubMed 

    Google Scholar 
    Yodzis, P. & Innes, S. Body size and consumer-resource dynamics. Am. Nat. 139, 1151–1175 (1992).
    Google Scholar 
    Prum, R. O. Interspecific social dominance mimicry in birds: social mimicry in birds. Zool. J. Linn. Soc. 172, 910–941 (2014).
    Google Scholar 
    Pyle, P. Identification Guide to North American Birds: A Compendium of Information on Identifying, Ageing, and Sexing ‘Near-Passerines’ and Passerines in the Hand (Slate Creek Press, 1997).Leys, C., Ley, C., Klein, O., Bernard, P. & Licata, L. Detecting outliers: do not use standard deviation around the mean, use absolute deviation around the median. J. Exp. Soc. Psychol. 49, 764–766 (2013).
    Google Scholar 
    Danielson, J. J. & Gesch, D. B. Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010) (US Geological Survey, 2011).Thornton, M. M. et al. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4 (ORNL Distributed Active Archive Center, 2020).Greenewalt, C. H. The flight of birds: the significant dimensions, their departure from the requirements for dimensional similarity, and the effect on flight aerodynamics of that departure. Trans. Am. Philos. Soc. 65, 1–67 (1975).
    Google Scholar 
    Longo, G. & Montévil, M. Perspectives on Organisms: Biological Time, Symmetries, and Singularities (Springer, 2014).Harvey, P. H. in Scaling in Biology (eds Brown, J. H. & West, G. B.) 253–265 (Oxford Univ. Press, 2000).Orme, D. et al. The caper package: comparative analysis of phylogenetics and evolution in R. R package version 5 (2013).R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2021).Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
    PubMed 

    Google Scholar 
    Nudds, R. L., Kaiser, G. W. & Dyke, G. J. Scaling of avian primary feather length. PLoS ONE 6, e15665 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nudds, R. Wing-bone length allometry in birds. J. Avian Biol. 38, 515–519 (2007).
    Google Scholar 
    Anderson, S. C., Branch, T. A., Cooper, A. B. & Dulvy, N. K. Black-swan events in animal populations. Proc. Natl Acad. Sci. USA 114, 3252–3257 (2017).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Stan Modeling Language Users Guide and Reference Manual, Version 2.18.0 (Stan Development Team, 2018); http://mc-stan.orgCarpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. 76, 1–32 (2017).Youngflesh, C. MCMCvis: tools to visualize, manipulate, and summarize MCMC output. J. Open Source Softw. 3, 640 (2018).
    Google Scholar 
    Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).
    Google Scholar 
    Gabry, J., Simpson, D., Vehtari, A., Betancourt, M. & Gelman, A. Visualization in Bayesian workflow. J. R. Stat. Soc. A 182, 389–402 (2019).
    Google Scholar 
    McElreath, R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman and Hall/CRC, 2018).Data Zone (BirdLife International, 2019); http://datazone.birdlife.org/species/requestdisCramp, S. & Brooks, D. Handbook of the Birds of Europe, the Middle East and North Africa. The Birds of the Western Palearctic, Vol. VI. Warblers (Oxford Univ. Press, 1992).Che-Castaldo, J., Che-Castaldo, C. & Neel, M. C. Predictability of demographic rates based on phylogeny and biological similarity. Conserv. Biol. 32, 1290–1300 (2018).PubMed 

    Google Scholar 
    Villemereuil, P., de, Wells, J. A., Edwards, R. D. & Blomberg, S. P. Bayesian models for comparative analysis integrating phylogenetic uncertainty. BMC Evol. Biol. 12, 102 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
    Google Scholar 
    Pagel, M. Inferring the historical patterns of biological evolution. Nature 401, 877–884 (1999).CAS 
    PubMed 

    Google Scholar 
    Hendry, A. P. & Kinnison, M. T. Perspective: the pace of modern life: measuring rates of contemporary microevolution. Evolution 53, 1637–1653 (1999).PubMed 

    Google Scholar 
    Gingerich, P. Rates of evolution: effects of time and temporal scaling. Science 222, 159–162 (1983).CAS 
    PubMed 

    Google Scholar 
    Bird, J. P. et al. Generation lengths of the world’s birds and their implications for extinction risk. Conserv. Biol. 34, 1252–1261 (2020).Gingerich, P. D. Rates of evolution. Annu. Rev. Ecol. Evol. Syst. 40, 657–675 (2009).
    Google Scholar 
    Bürger, R. & Lynch, M. Evolution and extinction in a changing environment: a quantitative-genetic analysis. Evolution 49, 151–163 (1995).PubMed 

    Google Scholar 
    Hendry, A. P., Farrugia, T. J. & Kinnison, M. T. Human influences on rates of phenotypic change in wild animal populations. Mol. Ecol. 17, 20–29 (2008).PubMed 

    Google Scholar  More

  • in

    Evapotranspiration frequently increases during droughts

    Ault, T. R. On the essentials of drought in a changing climate. Science 368, 256–260 (2020).CAS 

    Google Scholar 
    Rodell, M. et al. Emerging trends in global freshwater availability. Nature 557, 651–659 (2018).CAS 

    Google Scholar 
    Green, J. K. et al. Large influence of soil moisture on long-term terrestrial carbon uptake. Nature 565, 476–479 (2019).CAS 

    Google Scholar 
    Reichstein, M. et al. Climate extremes and the carbon cycle. Nature 500, 287–295 (2013).CAS 

    Google Scholar 
    Goulden, M. L. & Bales, R. C. California forest die-off linked to multi-year deep soil drying in 2012–2015 drought. Nat. Geosci. 12, 632–637 (2019).CAS 

    Google Scholar 
    Brodribb, T. J., Powers, J., Cochard, H. & Choat, B. Hanging by a thread? Forests and drought. Science 368, 261–266 (2020).CAS 

    Google Scholar 
    Seneviratne, S. I. et al. Investigating soil moisture–climate interactions in a changing climate: a review. Earth Sci. Rev. 99, 125–161 (2010).CAS 

    Google Scholar 
    Short Gianotti, D. J., Rigden, A. J., Salvucci, G. D. & Entekhabi, D. Satellite and station observations demonstrate water availability’s effect on continental-scale evaporative and photosynthetic land surface dynamics. Water Resour. Res. 55, 540–554 (2019).
    Google Scholar 
    Anderegg, W. R. L., Trugman, A. T., Bowling, D. R., Salvucci, G. & Tuttle, S. E. Plant functional traits and climate influence drought intensification and land–atmosphere feedbacks. Proc. Natl Acad. Sci. USA 116, 14071–14076 (2019).CAS 

    Google Scholar 
    Jung, M. et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply. Nature 467, 951–954 (2010).CAS 

    Google Scholar 
    Novick, K. A. et al. The increasing importance of atmospheric demand for ecosystem water and carbon fluxes. Nat. Clim. Chang. 6, 1023–1027 (2016).CAS 

    Google Scholar 
    Liu, Y., Kumar, M., Katul, G. G., Feng, X. & Konings, A. G. Plant hydraulics accentuates the effect of atmospheric moisture stress on transpiration. Nat. Clim. Chang. 10, 691–695 (2020).CAS 

    Google Scholar 
    Gupta, A., Rico-Medina, A. & Caño-Delgado, A. I. The physiology of plant responses to drought. Science 368, 266–269 (2020).CAS 

    Google Scholar 
    Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).CAS 

    Google Scholar 
    Teuling, A. J. et al. Evapotranspiration amplifies European summer drought. Geophys. Res. Lett. 40, 2071–2075 (2013).
    Google Scholar 
    Mastrotheodoros, T. et al. More green and less blue water in the Alps during warmer summers. Nat. Clim. Chang. 10, 155–161 (2020).
    Google Scholar 
    Peterson, T. J., Saft, M., Peel, M. C. & John, A. Watersheds may not recover from drought. Science 372, 745–749 (2021).CAS 

    Google Scholar 
    Helbig, M. et al. Increasing contribution of peatlands to boreal evapotranspiration in a warming climate. Nat. Clim. Chang. 10, 555–560 (2020).CAS 

    Google Scholar 
    Massmann, A., Gentine, P. & Lin, C. When does vapor pressure deficit drive or reduce evapotranspiration? J. Adv. Model. Earth Syst. 11, 3305–3320 (2019).
    Google Scholar 
    Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).
    Google Scholar 
    Orth, R. & Destouni, G. Drought reduces blue-water fluxes more strongly than green-water fluxes in Europe. Nat. Commun. 9, 3602 (2018).
    Google Scholar 
    Pendergrass, A. G. et al. Flash droughts present a new challenge for subseasonal-to-seasonal prediction. Nat. Clim. Chang. 10, 191–199 (2020).
    Google Scholar 
    Chu, H., Baldocchi, D. D., John, R., Wolf, S. & Reichstein, M. Fluxes all of the time? A primer on the temporal representativeness of FLUXNET. J. Geophys. Res. Biogeosci. 122, 289–307 (2017).
    Google Scholar 
    Ukkola, A. M. et al. Land surface models systematically overestimate the intensity, duration and magnitude of seasonal-scale evaporative droughts. Environ. Res. Lett. 11, 104012 (2016).
    Google Scholar 
    Trugman, A. T., Medvigy, D., Mankin, J. S. & Anderegg, W. R. L. Soil moisture stress as a major driver of carbon cycle uncertainty. Geophys. Res. Lett. 45, 6495–6503 (2018).
    Google Scholar 
    De Kauwe, M. G. et al. Do land surface models need to include differential plant species responses to drought? Examining model predictions across a mesic–xeric gradient in Europe. Biogeosciences 12, 7503–7518 (2015).
    Google Scholar 
    Dong, J., Lei, F. & Crow, W. T. Land transpiration–evaporation partitioning errors responsible for modeled summertime warm bias in the central United States. Nat. Commun. 13, 336 (2022).CAS 

    Google Scholar 
    Kennedy, D. et al. Implementing plant hydraulics in the Community Land Model, version 5. J. Adv. Model. Earth Syst. 11, 485–513 (2019).
    Google Scholar 
    Novick, K. A. et al. Confronting the water potential information gap. Nat. Geosci. 15, 158–164 (2022).CAS 

    Google Scholar 
    Liu, Y., Holtzman, N. M. & Konings, A. G. Global ecosystem-scale plant hydraulic traits retrieved using model–data fusion. Hydrol. Earth Syst. Sci. 25, 2399–2417 (2021).CAS 

    Google Scholar 
    Lin, Y.-S. et al. Optimal stomatal behaviour around the world. Nat. Clim. Chang. 5, 459–464 (2015).CAS 

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

    Google Scholar 
    Anderegg, W. R. L. et al. Hydraulic diversity of forests regulates ecosystem resilience during drought. Nature 561, 538–541 (2018).CAS 

    Google Scholar 
    Lehmann, P., Merlin, O., Gentine, P. & Or, D. Soil texture effects on surface resistance to bare-soil evaporation. Geophys. Res. Lett. 45, 10398–10405 (2018).
    Google Scholar 
    Fatichi, S. et al. Soil structure is an important omission in Earth System Models. Nat. Commun. 11, 522 (2020).CAS 

    Google Scholar 
    McCormick, E. L. et al. Widespread woody plant use of water stored in bedrock. Nature 597, 225–229 (2021).CAS 

    Google Scholar 
    Baldocchi, D., Ma, S. & Verfaillie, J. On the inter- and intra-annual variability of ecosystem evapotranspiration and water use efficiency of an oak savanna and annual grassland subjected to booms and busts in rainfall. Glob. Chang. Biol. 27, 359–375 (2021).CAS 

    Google Scholar 
    Condon, L. E., Atchley, A. L. & Maxwell, R. M. Evapotranspiration depletes groundwater under warming over the contiguous United States. Nat. Commun. 11, 873 (2020).CAS 

    Google Scholar 
    Maxwell, R. M. & Condon, L. E. Connections between groundwater flow and transpiration partitioning. Science 353, 377–380 (2016).CAS 

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

    Google Scholar 
    Mankin, J. S., Seager, R., Smerdon, J. E., Cook, B. I. & Williams, A. P. Mid-latitude freshwater availability reduced by projected vegetation responses to climate change. Nat. Geosci. 12, 983–988 (2019).CAS 

    Google Scholar 
    Strassburg, B. B. N. et al. Global priority areas for ecosystem restoration. Nature 586, 724–729 (2020).CAS 

    Google Scholar 
    Zhao, M. et al. Ecological restoration impact on total terrestrial water storage. Nat. Sustain. 4, 56–62 (2021).
    Google Scholar 
    Feng, X. et al. Revegetation in China’s Loess Plateau is approaching sustainable water resource limits. Nat. Clim. Chang. 6, 1019–1022 (2016).
    Google Scholar 
    Guan, K. et al. Photosynthetic seasonality of global tropical forests constrained by hydroclimate. Nat. Geosci. 8, 284–289 (2015).CAS 

    Google Scholar 
    Fan, Y., Li, H. & Miguez-Macho, G. Global patterns of groundwater table depth. Science 339, 940–943 (2013).CAS 

    Google Scholar 
    Zhao, M., Geruo, A., Velicogna, I. & Kimball, J. S. A global gridded dataset of GRACE drought severity index for 2002–14: comparison with PDSI and SPEI and a case study of the Australia Millennium Drought. J. Hydrometeorol. 18, 2117–2129 (2017).
    Google Scholar 
    Watkins, M. M., Wiese, D. N., Yuan, D.-N., Boening, C. & Landerer, F. W. Improved methods for observing Earth’s time variable mass distribution with GRACE using spherical cap mascons. J. Geophys. Res. Solid Earth 120, 2648–2671 (2015).
    Google Scholar 
    Wiese, D. N., Landerer, F. W. & Watkins, M. M. Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resour. Res. 52, 7490–7502 (2016).
    Google Scholar 
    Adler, R. F. et al. The Version-2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979–present). J. Hydrometeorol. 4, 1147–1167 (2003).
    Google Scholar 
    Sun, Q. et al. A review of global precipitation data sets: data sources, estimation, and intercomparisons. Rev. Geophys. 56, 79–107 (2018).
    Google Scholar 
    Gebremichael, M. et al. Error uncertainty analysis of GPCP monthly rainfall products: a data-based simulation study. J. Appl. Meteorol. 42, 1837–1848 (2003).
    Google Scholar 
    Rodell, M. et al. Basin scale estimates of evapotranspiration using GRACE and other observations. Geophys. Res. Lett. 31, L20504 (2004).
    Google Scholar 
    Major River Basins of the World (Global Runoff Data Centre, 2020).Pascolini-Campbell, M. A., Reager, J. T. & Fisher, J. B. GRACE-based mass conservation as a validation target for basin-scale evapotranspiration in the contiguous United States. Water Resour. Res. 56, e2019WR026594 (2020).
    Google Scholar 
    Fekete, B. M., Vörösmarty, C. J. & Grabs, W. High-resolution fields of global runoff combining observed river discharge and simulated water balances. Global Biogeochem. Cycles 16, 15-1–15-10 (2002).
    Google Scholar 
    Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).
    Google Scholar 
    Myneni, R., Knyazikhin, Y. & Park, T (ed. NASA EOSDIS Land Processes DAAC) (2021).Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
    Google Scholar 
    Sulla-Menashe, D. & Friedl, M. A. User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product (US Geological Survey, 2018).Zhao, M., Aa, G., Liu, Y. & Konings, A. Evapotranspiration frequently increases during droughts. Zenodo https://doi.org/10.5281/zenodo.6842054 (2022). More

  • in

    Recovery and genome reconstruction of novel magnetotactic Elusimicrobiota from bog soil

    Steen AD, Carini ACP, Lloyd KG, Thrash JC, Deangelis KM, Fierer N. High proportions of bacteria and archaea across most biomes remain uncultured. ISME J. 2019;13:3126–30.PubMed 
    PubMed Central 

    Google Scholar 
    Lloyd KG, Steen AD, Ladau J, Yin J. Phylogenetically novel uncultured microbial cells dominate earth microbiomes. mSystems. 2018;3:e00055–18.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Marcy Y, Ouverney C, Bik EM, Lo T, Ivanova N, Garcia H, et al. Dissecting biological “dark matter” with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc Natl Acad Sci USA. 2007;104:11889–94.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pascoal F, Costa R, Magalhães C. The microbial rare biosphere: current concepts, methods and ecological principles. FEMS Microbiol Ecol. 2021;97:fiaa227.CAS 
    PubMed 

    Google Scholar 
    Sogin ML, Morrison HG, Huber JA, Welch DM, Huse SM, Neal PR, et al. Microbial diversity in the deep sea and the underexplored “rare biosphere”. Proc Natl Acad Sci USA. 2006;103:12115–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gareev KG, Grouzdev DS, Kharitonskii PV, Kosterov A, Koziaeva VV, Sergienko ES, et al. Magnetotactic bacteria and magnetosomes: basic properties and applications. Magnetochemistry. 2021;7:86.CAS 

    Google Scholar 
    Lefevre CT, Bazylinski DA. Ecology, diversity, and evolution of magnetotactic bacteria. Microbiol Mol Biol Rev. 2013;77:497–526.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lin W, Pan Y, Bazylinsky DA. Diversity and ecology of and biomineralization by magnetotactic bacteria. Environ Microbiol Rep. 2017;9:345–56.CAS 
    PubMed 

    Google Scholar 
    Uebe R, Schüler D. Magnetosome biogenesis in magnetotactic bacteria. Nat Rev Microbiol. 2016;14:621–37.CAS 
    PubMed 

    Google Scholar 
    Lefèvre CT, Frankel RB, Bazylinski DA. Magnetotaxis in prokaryotes. eLS. 2011. https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2F9780470015902.a0000397.pub2https://onlinelibrary.wiley.com/action/showCitFormats?doi=10.1002%2F9780470015902.a0000397.pub2.Goswami P, He K, Li J, Pan Y, Roberts AP, Lin W. Magnetotactic bacteria and magnetofossils: ecology, evolution and environmental implications. npj Biofilms Microbiomes. 2022;8:43.PubMed 
    PubMed Central 

    Google Scholar 
    Flies CB, Jonkers HM, De Beer D, Bosselmann K, Böttcher ME, Schüler D. Diversity and vertical distribution of magnetotactic bacteria along chemical gradients in freshwater microcosms. FEMS Microbiol Ecol. 2005;52:185–95.CAS 
    PubMed 

    Google Scholar 
    Wolfe RS, Thauer RK, Pfennig N. A’capillary racetrack’ method for isolation of magnetotactic bacteria. FEMS Microbiol Ecol. 1987;45:31–5.
    Google Scholar 
    Jogler C, Lin W, Meyerdierks A, Kube M, Katzmann E, Flies C, et al. Toward cloning of the magnetotactic metagenome: identification of magnetosome island gene clusters in uncultivated magnetotactic bacteria from different aquatic sediments. Appl Environ Microbiol. 2009;75:3972–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Lin W, Zhang W, Paterson GA, Zhu Q, Zhao X. Expanding magnetic organelle biogenesis in the domain Bacteria. Microbiome. 2020;8:152.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Geissinger O, Herlemann DPR, Mo E, Maier UG, Brune A. The ultramicrobacterium “Elusimicrobium minutum” gen. nov., sp. nov., the first cultivated representative of the Termite Group 1 phylum. Appl Environ Microbiol. 2009;75:2831–40.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wakako I-O, Brune A. Cospeciation of termite gut flagellates and their bacterial endosymbionts: Trichonympha species and ‘Candidatus Endomicrobium trichonymphae’. Mol Ecol. 2009;18:332–42.
    Google Scholar 
    Zheng H, Dietrich C, Radek R, Brune A. Endomicrobium proavitum, the first isolate of Endomicrobia class. nov. (phylum Elusimicrobia) – an ultramicrobacterium with an unusual cell cycle that fixes nitrogen with a Group IV nitrogenase. Environ Ecol Stat. 2016;18:191–204.CAS 

    Google Scholar 
    Méheust R, Castelle CJ, Carnevali PBM, Chen L, Amano Y, Hug LA, et al. Groundwater Elusimicrobia are metabolically diverse compared to gut microbiome Elusimicrobia and some have a novel nitrogenase paralog. ISME J. 2020;14:2907–22.PubMed 
    PubMed Central 

    Google Scholar 
    Lin H, Ascher DB, Myung Y, Lamborg CH, Hallam SJ, Gionfriddo CM, et al. Mercury methylation by metabolically versatile and cosmopolitan marine bacteria. ISME J. 2021;15:1810–25.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Parks DH, Rinke C, Chuvochina M, Chaumeil PA, Woodcroft BJ, Evans PN, et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017;2:1533–42.CAS 
    PubMed 

    Google Scholar 
    Zhang L, Gong X, Wang L, Guo K, Cao S, Zhou Y. Science of the total environment metagenomic insights into the effect of thermal hydrolysis pre-treatment on microbial community of an anaerobic digestion system. Sci Total Environ. 2021;791:148096.CAS 
    PubMed 

    Google Scholar 
    Woodcroft BJ, Singleton CM, Boyd JA, Evans PN, Emerson JB, Zayed AAF, et al. Genome-centric view of carbon processing in thawing permafrost. Nature. 2018;560:49–54.CAS 
    PubMed 

    Google Scholar 
    Uzun M, Alekseeva L, Krutkina M, Koziaeva V, Grouzdev D. Unravelling the diversity of magnetotactic bacteria through analysis of open genomic databases. Sci Data. 2020;7:252.PubMed 
    PubMed Central 

    Google Scholar 
    Tully BJ, Wheat CG, Glazer BT, Huber JA. A dynamic microbial community with high functional redundancy inhabits the cold, oxic subseafloor aquifer. ISME J. 2018;12:1–16.CAS 
    PubMed 

    Google Scholar 
    Kirillova NP, Sileva TM, Ul’yanova TY, Rozov SY, Il’yashenko MA, Makarov MI. Digital soil map of Chashnikovo training and experimental soil ecological center, Moscow State University. Mosc Univ Soil Sci Bull. 2015;70:58–65.
    Google Scholar 
    Koziaeva VV, Alekseeva LM, Uzun MM, Leão P, Sukhacheva MV, Patutina EO, et al. Biodiversity of magnetotactic bacteria in the freshwater lake Beloe Bordukovskoe, Russia. Microbiology. 2020;89:348–58.CAS 

    Google Scholar 
    Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–1.CAS 
    PubMed 

    Google Scholar 
    Edgar RC, Flyvbjerg H. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics. 2015;31:3476–82.CAS 
    PubMed 

    Google Scholar 
    Edgar RC. UNOISE2: improved error-correction for Illumina 16S and ITS amplicon sequencing. bioRxiv 2016. https://doi.org/10.1101/081257.Pruesse E, Peplies J, Glöckner FO. SINA: Accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Fierer N, Jackson JA, Vilgalys R, Jackson RB. Assessment of soil microbial community structure by use of taxon-specific quantitative PCR assays. Appl Environ Microbiol. 2005;71:4117–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. MetaSPAdes: a new versatile metagenomic assembler. Genome Res. 2017;27:824–34.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Wu YW, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.CAS 
    PubMed 

    Google Scholar 
    Kang DD, Li F, Kirton E, Thomas A, Egan R, An H, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.PubMed 
    PubMed Central 

    Google Scholar 
    Lin HH, Liao YC. Accurate binning of metagenomic contigs via automated clustering sequences using information of genomic signatures and marker genes. Sci Rep. 2016;6:12–9.
    Google Scholar 
    Sieber CMK, Probst AJ, Sharrar A, Thomas BC, Hess M, Tringe SG, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29:1072–5.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Chaumeil P, Mussig AJ, Parks DH, Hugenholtz P. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–7.PubMed Central 

    Google Scholar 
    Tatusova T, Dicuccio M, Badretdin A, Chetvernin V, Nawrocki EP, Zaslavsky L, et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 2016;44:6614–24.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Ji R, Zhang W, Pan Y, Lin W. MagCluster: a tool for identification, annotation, and visualization of magnetosome gene clusters. Microbiol Resour Announc. 2022;11:e01031–21.CAS 
    PubMed Central 

    Google Scholar 
    Wu S, Zhu Z, Fu L, Niu B, Li W. WebMGA: a customizable web server for fast metagenomic sequence analysis. BMC Genomics. 2011;12:444.PubMed 
    PubMed Central 

    Google Scholar 
    Kanehisa M, Sato Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci. 2020;29:28–35.CAS 
    PubMed 

    Google Scholar 
    Shaffer M, Borton MA, McGivern BB, Zayed AA, La Rosa SL. 0003 3527 8101, Solden LM, et al. DRAM for distilling microbial metabolism to automate the curation of microbiome function. Nucleic Acids Res. 2020;48:8883–900.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9:5114.PubMed 
    PubMed Central 

    Google Scholar 
    Nguyen LT, Schmidt HA, Von Haeseler A, Minh BQ. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol Biol Evol. 2015;32:268–74.CAS 
    PubMed 

    Google Scholar 
    Kalyaanamoorthy S, Minh BQ, Wong TKF, Haeseler AVon, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14:587–9.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hoang DT, Chernomor O, Von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the ultrafast bootstrap approximation. Mol Biol Evol. 2018;35:518–22.CAS 
    PubMed 

    Google Scholar 
    Letunic I, Bork P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49:W293–6.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Coleman GA, Davín AA, Mahendrarajah TA, Szánthó LL, Spang A, Hugenholtz P, et al. A rooted phylogeny resolves early bacterial evolution. Science. 2021;372:eabe0511.CAS 
    PubMed 

    Google Scholar 
    Parks DH. https://github.com/dparks1134/CompareM.Dombrowski N, Lee JH, Williams TA, Offre P, Spang A. Genomic diversity, lifestyles and evolutionary origins of DPANN archaea. FEMS Microbiol Lett. 2019;366:fnz008.CAS 
    PubMed Central 

    Google Scholar 
    Lin W, Zhang W, Zhao X, Roberts AP, Paterson GA, Bazylinski DA, et al. Genomic expansion of magnetotactic bacteria reveals an early common origin of magnetotaxis with lineage-specific evolution. ISME J. 2018;12:1508–19.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Urakawa H, Garcia JC, Nielsen JL, Le VQ, Kozlowski JA, Stein LY, et al. Nitrosospira lacus sp. nov., a psychrotolerant, ammonia-oxidizing bacterium from sandy lake sediment. Int J Syst Evol Microbiol. 2015;65:242–50.CAS 
    PubMed 

    Google Scholar 
    Kalyuzhnaya MG, De Marco P, Bowerman S, Pacheco CC, Lara JC, Lidstrom ME, et al. Methyloversatilis universalis gen. nov., sp. nov., a novel taxon within the Betaproteobacteria represented by three methylotrophic isolates. Int J Syst Evol Microbiol. 2006;56:2517–22.CAS 
    PubMed 

    Google Scholar 
    Bazylinski DA, Frankel RB, Konhauser KO. Modes of biomineralization of magnetite by microbes. Geomicrobiol J. 2007;24:465–75.CAS 

    Google Scholar 
    Uzun M, Koziaeva V, Dziuba M, Leão P, Krutkina M, Grouzdev D. Detection of interphylum transfers of the magnetosome gene cluster in magnetotactic bacteria. Front Microbiol. 2022;13:945734.PubMed 
    PubMed Central 

    Google Scholar 
    Parks DH, Chuvochina M, Waite DW, Rinke C, Skarshewski A, Chaumeil PA, et al. A standardized bacterial taxonomy based on genome phylogeny substantially revises the tree of life. Nat Biotechnol. 2018;36:996.CAS 
    PubMed 

    Google Scholar 
    Murphy CL, Biggerstaff J, Eichhorn A, Ewing E, Shahan R, Soriano D, et al. Genomic characterization of three novel Desulfobacterota classes expand the metabolic and phylogenetic diversity of the phylum. Environ Microbiol. 2021;23:4326–43.CAS 
    PubMed 

    Google Scholar 
    Konstantinidis KT, Rosselló-Móra R, Amann R. Uncultivated microbes in need of their own taxonomy. ISME J. 2017;11:2399–406.PubMed 
    PubMed Central 

    Google Scholar 
    Denise R, Abby SS, Rocha EPC. Diversification of the type IV filament superfamily into machines for adhesion, protein secretion, DNA uptake, and motility. PLoS Biol. 2019;17:e3000390.PubMed 
    PubMed Central 

    Google Scholar 
    Hennell James R, Deme JC, Kjӕr A, Alcock F, Silale A, Lauber F, et al. Structure and mechanism of the proton-driven motor that powers type 9 secretion and gliding motility. Nat Microbiol. 2021;6:221–33.CAS 
    PubMed 

    Google Scholar 
    Nolan LM, Whitchurch CB, Barquist L, Katrib M, Boinett CJ, Mayho M, et al. A global genomic approach uncovers novel components for twitching motility-mediated biofilm expansion in Pseudomonas aeruginosa. Micro Genomics. 2018;4:e000229.
    Google Scholar 
    Uzun M, Koziaeva V, Dziuba M, Alekseeva L, Grouzdev D. Mam protein trees. 2022. https://doi.org/10.6084/m9.figshare.c.6045158.v1.Arnoux P, Siponen MI, Lefèvre CT, Ginet N, Pignol D. Structure and evolution of the magnetochrome domains: no longer alone. Front Microbiol. 2014;5:117.PubMed 
    PubMed Central 

    Google Scholar 
    Katzmann E, Scheffel A, Gruska M, Plitzko JM, Schüler D. Loss of the actin-like protein MamK has pleiotropic effects on magnetosome formation and chain assembly in Magnetospirillum gryphiswaldense. Mol Microbiol. 2010;77:208–24.CAS 
    PubMed 

    Google Scholar 
    Wagner-Döbler I, Bennasar A, Vancanneyt M, Strömpl C, Brümmer I, Eichner C, et al. Microcosm enrichment of biphenyl-degrading microbial communities from soils and sediments. Appl Environ Microbiol. 1998;64:3014–22.PubMed 
    PubMed Central 

    Google Scholar 
    Ibekwe AM, Papiernik SK, Gan J, Yates SR, Crowley DE, Yang CH. Microcosm enrichment of 1,3-dichloropropene-degrading soil microbial communities in a compost-amended soil. J Appl Microbiol. 2001;91:668–76.CAS 
    PubMed 

    Google Scholar 
    Yakimov MM, Denaro R, Genovese M, Cappello S, D’Auria G, Chernikova TN, et al. Natural microbial diversity in superficial sediments of Milazzo Harbor (Sicily) and community successions during microcosm enrichment with various hydrocarbons. Environ Microbiol. 2005;7:1426–41.CAS 
    PubMed 

    Google Scholar 
    Tringe SG, Von Mering C, Kobayashi A, Salamov AA, Chen K, Chang HW, et al. Comparative metagenomics of microbial communities. Science. 2005;308:554–7.CAS 
    PubMed 

    Google Scholar 
    Lefèvre CT, Trubitsyn D, Abreu F, Kolinko S, Jogler C, de Almeida LGP, et al. Comparative genomic analysis of magnetotactic bacteria from the Deltaproteobacteria provides new insights into magnetite and greigite magnetosome genes required for magnetotaxis. Environ Microbiol. 2013;15:2712–35.PubMed 

    Google Scholar 
    Wadhwa N, Berg HC. Bacterial motility: machinery and mechanisms. Nat Rev Microbiol. 2022;20:161–73.CAS 
    PubMed 

    Google Scholar 
    Zhu K, Pan H, Li J, Yu-Zhang K, Zhang SD, Zhang WY, et al. Isolation and characterization of a marine magnetotactic spirillum axenic culture QH-2 from an intertidal zone of the China Sea. Res Microbiol. 2010;161:276–83.CAS 
    PubMed 

    Google Scholar 
    Kaimer C, Zusman DR. Regulation of cell reversal frequency in Myxococcus xanthus requires the balanced activity of CheY-like domains in FrzE and FrzZ. Mol Microbiol. 2016;100:379–95.CAS 
    PubMed 

    Google Scholar 
    Kühn MJ, Talà L, Inclan YF, Patino R, Pierrat X, Vos I, et al. Mechanotaxis directs Pseudomonas aeruginosa twitching motility. Proc Natl Acad Sci USA. 2021;118:e2101759118.PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Exceptional preservation of internal organs in a new fossil species of freshwater shrimp (Caridea: Palaemonoidea) from the Eocene of Messel (Germany)

    De Grave, S., & Fransen, C. H. J. M. Carideorum Catalogus: The Recent Species of the Dendrobranchiate, Stenopodidean, Procarididean and Caridean Shrimps (Crustacea: Decapoda). Zool. Meded. 85, (2011).Garassino, A. The macruran decapod crustaceans of the Lower Cretaceous (Lower Barremian) of Las Hoyas (Cuenca, Spain). Atti Soc. it. Sci. nat. Museo civ. Stor. nat. Milano 137, 101–126 (1997).Bravi, S., Coppa, M. G., Garassino, A., & Patricelli, R. Palaemon vesolensis n. sp. (Crustacea, Decapoda) from the Plattenkalk of Vesole Mount (Salerno, Southern Italy). Atti Soc. it. Sci. nat. Museo civ. Stor. nat. Milano 140, 141–169 (1999).Colleary, C. et al. Chemical, experimental, and morphological evidence for diagenetically altered melanin in exceptionally preserved fossils. Proc. Natl. Acad. Sci. U.S.A. 11241, 12592–12597 (2015).Article 
    ADS 

    Google Scholar 
    Vinther, J., Briggs, D. E., Clarke, J., Mayr, G. & Prum, R. O. Structural coloration in a fossil feather. Biol. Lett. 6, 128–131 (2010).Article 
    PubMed 

    Google Scholar 
    McNamara, M. E. et al. Fossilised biophotonic nanostructures reveal the original colors of 47 million-year-old moths. PLoS Biol. 9, e1001200 (2011).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rietschel, S. Taphonomic biasing in the Messel Fauna and Flora. Cour. Forsch. Inst. Senckenberg 107, 169–182 (1988).
    Google Scholar 
    Wolf, H. W. Schätze im Schiefer (Westermann, 1991).Rabenstein, R. Messel 2000 – Das Weltnaturerbe Deutschlands (eds Forschungsinstitut Senckenberg) (2000).Gruber, G., & Micklich, N. Messel – Treasures of the Eocene (Hessisches Landesmuseum Darmstadt, 2007).Wedmann, S. Annotated taxon-list of the invertebrate animals from the Eocene fossil site Grube Messel near Darmstadt Germany. Cour. Forsch. Inst. Senckenberg 255, 103–110 (2005).
    Google Scholar 
    Schaal, S. F. K. & Rabenstein, R. D. Tagebau Messel in Linien und Zahlen. Senckenberg Nat. Forsch. Mus. 142, 376–377 (2012).
    Google Scholar 
    Moshayedi, M., Lenz, O. K., Wilde, V. & Hinderer, M. The recolonisation of volcanically disturbed Eocene habitats of Central Europe: the maar lakes of Messel and Offenthal (SW Germany) compared. Paleobiodivers. Paleoenviron. 100, 951–973 (2020).Article 

    Google Scholar 
    Schulz, R., Harms, F.-J. & Felder, M. Die Forschungsbohrung Messel 2001: Ein Beitrag zur Entschlüsselung der Genese einer Ölschieferlagerstätte. Z. angew. Geol. 2002, 9–17 (2002).
    Google Scholar 
    Felder, M. & Harms, F. J. Lithologie und genetische Interpretation der vulkano-sedimentären Ablagerungen aus der Grube Messel anhand der Forschungsbohrung Messel 2001 und weiterer Bohrungen (Eozän, Messel-Formation, Sprendlinger Horst, Südhessen). Cour. Forsch. Inst. Senckenberg 252, 151–203 (2004).
    Google Scholar 
    Büchel, G. N., & Schaal, S. F. K. The formation of the Messel maar in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 62–103 (Schweizerbart, 2018).Der, G. K. Messeler Ölschiefer – ein Algenlaminit. Cour. Forsch. Inst. Senckenberg 131, 1–143 (1990).
    Google Scholar 
    Lenz, O. K., Wilde, V. & Riegel, W. Recolonization of a Middle Eocene volcanic site: quantitative palynology of the initial phase of the maar lake of Messel (Germany). Rev. Palaeobot. Palynol. 145, 217–242 (2007).Article 

    Google Scholar 
    Bauersachs, T., Schouten, S. & Schwark, L. Characterization of the sedimentary organic matter preserved in Messel oil shale by bulk geochemistry and stable isotopes. Palaeogeogr. Palaeoclimatol. Palaeoecol. 410, 390–400 (2014).Article 

    Google Scholar 
    Mertz, D. F. & Renne, P. R. A numerical age for the Messel fossil deposit (UNESCO world natural heritage site) from 40Ar/39Ar dating. Cour. Forsch. Inst. Senckenberg 255, 67–75 (2005).
    Google Scholar 
    Lenz, O. K., Wilde, V., Mertz, D. F. & Riegel, W. New palynology-based astronomical and revised 40Ar/39Ar ages for the Eocene maar lake of Messel (Germany). Int. J. Earth Sci. 104, 873–889 (2015).Article 
    CAS 

    Google Scholar 
    Lenz, O. K. & Wilde, V. Changes in Eocene plant diversity and composition of vegetation: The lacustrine archive of Messel (Germany). Paleobiology 44, 709–735 (2018).Article 

    Google Scholar 
    Lenz, O. K., Wilde, V, Riegel, W., & Harms, F-J. A 600 k.y. record of El Niño–Southern Oscillation (ENSO): evidence for persisting teleconnections during the Middle Eocene greenhouse climate of Central Europe. Geology 38, 627–630 (2010).Lenz, O. K., Wilde, V, & Riegel, W. Paleoclimate – Learning from the past for the future in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 16–23 (Schweizerbart, 2018).Grein, M., Utescher, T., Wilde, V. & Roth-Nebelsick, A. Reconstruction of the middle Eocene climate of Messel using palaeobotanical data. Neues Jb. Geol. Paläontol. Abh. 260, 305–318 (2011).Article 

    Google Scholar 
    Tütken, T. Isotope compositions (C, O, Sr, Nd) of vertebrate fossils from the Middle Eocene oil shale of Messel, Germany: Implications for their taphonomy and palaeoenvironment. Palaeogeogr. Palaeoclimatol. Palaeoecol. 416, 92–109 (2014).Article 

    Google Scholar 
    Wilde, V. The fossil flora of Messel in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 42–61 (Schweizerbart, 2018).Smith, K. T., Schaal, S. F. K. & Habersetzer, J. (eds.) Messel: An Ancient Greenhouse Ecosystem. (Schweizerbart, 2018).Wedmann, S., Hörnschemeyer, T., Engel, M. S., Zetter, R. & Grímsson, F. The last meal of an Eocene pollen-feeding fly. Curr. Biol. 31, 2020–2026 (2021).Article 
    CAS 
    PubMed 

    Google Scholar 
    Wedmann, S. Jewels in the oil shale – insects and other invertebrates in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 62–103 (Schweizerbart, 2018).Franzen J. L. Odd-toed ungulates – Early horses and tapiromorphs in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 292–301 (Schweizerbart, 2018).Franzen, J. L., Aurich, C. & Habersetzer, J. Description of a well preserved fetus of the European Eocene Equoid Eurohippus messelensis. PLoS ONE 10, e0137985 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Franzen J. L., & Gingerich, P. D. Primates – Rareties in Messel in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 240–247 (Schweizerbart, 2018).Franzen, J. L. et al. Complete primate skeleton from the middle Eocene of Messel in Germany: Morphology and paleobiology. PLoS ONE 4(5), e5723 (2009).Article 
    ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Houša, V. Bechleja inopinata n. g., n. sp., nový ráček z českých třetihor (Decapoda, Palaemonidae). Ústřed. Ústavu Geol. Sborník 23, 365–377 (1957).Glaessner, M. F. Decapoda. In Part R Arthropoda 4(2) Treatise on Invertebrate Paleontology (ed Moore, R. C.) (The University of Kansas Press and The Geological Society of America, 1969).De Grave, S., Cai, Y. & Anker, A. Global diversity of shrimps (Crustacea: Decapoda: Caridea) in freshwater. Hydrobiologia 595, 287–293 (2008).Article 

    Google Scholar 
    Garassino, A. & Bravi, S. Palaemon antonellae new species (Crustacea, Decapoda, Caridea) from the Lower Cretaceous “Platydolomite” of profeti (Caserta, Italy). J. Paleontol. 77, 589–592 (2003).Article 

    Google Scholar 
    Schweitzer, C., Karasawa, H., Schweigert, G., Feldmann, R. & Garassino, A. Systematic list of fossil decapod crustacean species. Crustac. Monogr. 10, 1–222 (2010).
    Google Scholar 
    Plotnick, R. E. Taphonomy of a modern shrimp: implications for the arthropod fossil record. Palaios 1, 286–293 (1986).Article 
    ADS 

    Google Scholar 
    Klompmaker, A. A., Portell, R. W. & Frick, M. G. Comparative experimental taphonomy of eight marine arthropods indicates distinct differences in preservation potential. Palaeontology 60, 773–794 (2017).Article 

    Google Scholar 
    Vannier, J., Schoenemann, B., Gillot, T., Charbonnier, S. & Clarkson, E. Exceptional preservation of eye structure in arthropod visual predators from the Middle Jurassic. Nat. Commun. 7, 1–9 (2016).Article 

    Google Scholar 
    Jauvion, C., Audo, D., Charbonnier, S. & Vannier, J. Virtual dissection and lifestyle of a 165-million-year-old female polychelidan lobster. Arthropod Struct. Dev. 45, 122–132 (2016).Article 
    PubMed 

    Google Scholar 
    Pazinato, P. G., Jauvion, C., Schweigert, G., Haug, J. T. & Haug, C. After 100 years: a detailed view of an eumalacostracan crustacean from the Upper Jurassic Solnhofen Lagerstätte with raptorial appendages unique to Euarthropoda. Lethaia 54, 55–72 (2021).Article 

    Google Scholar 
    Briggs, D. E. G. & Kear, A. J. Decay and mineralization of shrimps. Palaios 9, 431–456 (1994).Article 
    ADS 

    Google Scholar 
    Wuttke, M. Conservation-dissolution-transformation. On the behaviour of biogenic materials during fossilization In Messel: an insight into the history of life and of the earth (eds. Schaal, S. & Ziegler, W.) 263–275 (Claredon, 1992).Thompson, J. R. Comments on phylogeny of section Caridea (Decapoda Natantia) and the phylogenetic importance of the Oplophoridea. Proc. Symp. Crustacea Part 1, 314–326 (1967).
    Google Scholar 
    Ashelby, C. W., De Grave, S. & Johnson, M. L. Preliminary observations on the mandibles of palaemonoid shrimp (Crustacea: Decapoda: Caridea: Palaemonoidea). PeerJ 3, e846 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Felgenhauer, B. E., & Abele, L. G. Phylogenetic relationships among shrimp-like decapods. In Crustacean Phylogeny (ed Schram, F. R.) 291–311 (A. A. Balkema, 1983).Wowor, D., Cai, Y., & Ng, P. K. L. Crustacea: Decapoda, Caridea. In Freshwater Invertebrates of the Malaysian Region (eds Yule, C. M. & Y. H. Sen, Y. H.) 337–357 (Academy of Sciences Malaysia, 2004).Rodd, F. H., & Reznick, D. N. Life History Evolution in Guppies: III. The Impact of Prawn Predation on Guppy Life Histories. Oikos 62, 13–19 (1991).Felgenhauer, B. E. & Abele, L. G. Feeding structures of two atyid shrimps, with comments on Caridean phylogeny. J. Crustac. Biol. 5, 397–419 (1985).Article 

    Google Scholar 
    de Mazancourt, V., Marquet, G., & Keith, P. The “Pinocchio-shrimp effect”: First evidence of variation in rostrum length with the environment in Caridina H. Milne-Edwards, 1837 (Decapoda: Caridea: Atyidae). J. Crustac. Biol. 37, 249–257 (2017).Zimmermann, G. et al. Geometric morphometrics of carapace of Macrobrachium australe (Crustacea: Palaemonidae) from Reunion Island. Acta Zool. 93, 492–500 (2012).Article 

    Google Scholar 
    Bauer, R. T. Amphidromy in shrimps: a life cycle between rivers and the sea. Lat. Am. J. Aquat. Res. 41, 633–650 (2013).Article 

    Google Scholar 
    Jalihal, D. R., Sankolli, K. N. & Shenoy, S. Evolution of larval developmental patterns and the process of freshwaterization in the prawn genus Macrobrachium Bate, 1868 (Decapoda, Palaemonidae). Crustaceana 65, 365–376 (1993).Article 

    Google Scholar 
    Grande, L. Paleontology of the Green River Formation, with a review of the fish fauna. Bull. Geol. Surv. Wyoming 63, 1–333 (1984).
    Google Scholar 
    Grande, L. The Lost World of Fossil Lake: snapshots from deep time (University of Chicago Press, 2013).Micklich, N. Peculiarities of the Messel fish fauna and their palaeoecological implications: A case study. Palaeobiodivers. Palaeoenviron. 92, 585–629 (2012).Article 

    Google Scholar 
    Micklich, N. Actinopterygians—the fishes of the Messel lake. in Messel: An Ancient Greenhouse Ecosystem (eds. Smith, K. T., Schaal, S. F. K. & Habersetzer, J.) 104–111 (Schweizerbart, 2018).Christodoulou, M., Anastasiadou, C., Jugovic, J., & Tzomos, T. Freshwater Shrimps (Atyidae, Palaemonidae, Typhlocarididae) in the Broader Mediterranean Region: Distribution, Life Strategies, Threats, Conservation Challenges and Taxonomic Issues. In A Global Overview of the Conservation of Freshwater Decapod Crustaceans (eds Kawai, T. & Cumberlidge, N.) 199–236 (Springer, 2016).Anger, K. Neotropical Macrobrachium (Caridea: Palaemonidae): On the biology, origin, and radiation of freshwater-invading shrimp. J. Crustac. Biol. 33, 151–183 (2013).Article 

    Google Scholar  More

  • in

    Ecological and evolutionary trends of body size in Pristimantis frogs, the world's most diverse vertebrate genus

    LaBarbera, M. The evolution and ecology of body size. In Patterns and Processes in the History of Life (eds Raup, D. M. & Jablonski, D.) 69–98 (Springer, 1986).
    Google Scholar 
    Peters, R. H. & Peters, R. H. The Ecological Implications of Body Size Vol. 2 (Cambridge University Press, 1986).
    Google Scholar 
    Klingenberg, C. P. & Spence, J. On the role of body size for life-history evolution. Ecol. Entomol. 22(1), 55–68 (1997).
    Google Scholar 
    Blanckenhorn, W. U. The evolution of body size: What keeps organisms small?. Q. Rev. Biol. 75(4), 385–407 (2000).CAS 
    PubMed 

    Google Scholar 
    Sibly, R. M. & Brown, J. H. Effects of body size and lifestyle on evolution of mammal life histories. Proc. Natl. Acad. Sci. USA 104(45), 17707–17712 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Schmidt-Nielsen, K. Scaling in biology: The consequences of size. J. Exp. Zool. 194(1), 287–307 (1975).CAS 
    PubMed 

    Google Scholar 
    Calder, W. A. Size, Function, and Life History (Courier Corporation, 1996).
    Google Scholar 
    Gould, S. J. Allometry and size in ontogeny and phylogeny. Biol. Rev. 41(4), 587–638 (1966).CAS 
    PubMed 

    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(5538), 2248–2251 (2001).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Gearty, W. & Payne, J. L. Physiological constraints on body size distributions in Crocodyliformes. Evolution 74(2), 245–255 (2020).PubMed 

    Google Scholar 
    Maurer, B. A., Brown, J. H. & Rusler, R. D. The micro and macro in body size evolution. Evolution 46(4), 939–953 (1992).PubMed 

    Google Scholar 
    Hone, D. W. & Benton, M. J. The evolution of large size: How does Cope’s Rule work?. Trends Ecol. Evol. 20(1), 4–6 (2005).PubMed 

    Google Scholar 
    Reeve, J. P. & Fairbairn, D. J. Predicting the evolution of sexual size dimorphism. J. Evol. Biol. 14(2), 244–254 (2001).
    Google Scholar 
    Blanckenhorn, W. U. Behavioral causes and consequences of sexual size dimorphism. Ethology 111(11), 977–1016 (2005).
    Google Scholar 
    Wu, H., Jiang, T., Huang, X. & Feng, J. Patterns of sexual size dimorphism in horseshoe bats: Testing Rensch’s rule and potential causes. Sci. Rep. 8(1), 1–13 (2018).ADS 

    Google Scholar 
    Cox, R. M., Butler, M. A. & John-Alder, H. B. The evolution of sexual size dimorphism in reptiles. In Sex, Size and Gender Roles: Evolutionary Studies of Sexual Size Dimorphism (eds Fairbairn, D. J. et al.) 38–49 (Oxford University Press, 2007).
    Google Scholar 
    Stillwell, R. C., Blanckenhorn, W. U., Teder, T., Davidowitz, G. & Fox, C. W. Sex differences in phenotypic plasticity affect variation in sexual size dimorphism in insects: From physiology to evolution. Annu. Rev. Entomol. 55, 227–245 (2010).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rensch, B. Die Abhängigkeit der relativen Sexualdifferenz von der Körpergrösse. Bonn. Zool. Beitr. 1, 58–69 (1950).
    Google Scholar 
    Rensch, B. Evolution Above the Species Level (Columbia University Press, 1960).
    Google Scholar 
    Shine, R. Ecological causes for the evolution of sexual dimorphism: A review of the evidence. Q. Rev. Biol. 64(4), 419–461 (1989).CAS 
    PubMed 

    Google Scholar 
    Portik, D. M., Blackburn, D. C. & McGuire, J. A. Macroevolutionary patterns of sexual size dimorphism among African tree frogs (Family: Hyperoliidae). J. Hered. 111(4), 379–391 (2020).PubMed 

    Google Scholar 
    Ceballos, C. P., Adams, D. C., Iverson, J. B. & Valenzuela, N. Phylogenetic patterns of sexual size dimorphism in turtles and their implications for Rensch’s rule. Evol. Biol. 40(2), 194–208 (2013).
    Google Scholar 
    Amado, T. F., Martinez, P. A., Pincheira-Donoso, D. & Olalla-Tárraga, M. Á. Body size distributions of anurans are explained by diversification rates and the environment. Glob. Ecol. Biogeogr. 30(1), 154–164 (2021).
    Google Scholar 
    Starostová, Z., Kubička, L. & Kratochvíl, L. Macroevolutionary pattern of sexual size dimorphism in geckos corresponds to intraspecific temperature-induced variation. J. Evol. Biol. 23(4), 670–677 (2010).PubMed 

    Google Scholar 
    Herczeg, G., Gonda, A. & Merilä, J. Rensch’s rule inverted–female-driven gigantism in nine-spined stickleback Pungitius pungitius. J. Anim. Ecol. 79(3), 581–588 (2010).PubMed 

    Google Scholar 
    Liao, W. B. & Chen, W. Inverse Rensch’s rule in a frog with female-biased sexual size dimorphism. Naturwissenschaften 99(5), 427–431 (2012).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Cooper, M. I. Sexual size dimorphism and the rejection of Rensch’s rule in Diplopoda (Arthropoda). J. Entomol. Zool. Stud. 6(1), 1582–1587 (2018).
    Google Scholar 
    Cheng, R. C. & Kuntner, M. Phylogeny suggests nondirectional and isometric evolution of sexual size dimorphism in argiopine spiders. Evolution 68(10), 2861–2872 (2014).PubMed 

    Google Scholar 
    Webb, T. J. & Freckleton, R. P. Only half right: Species with female-biased sexual size dimorphism consistently break Rensch’s rule. PLoS ONE 2(9), e897 (2007).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Gaston, K. J., Chown, S. L. & Evans, K. L. Ecogeographical rules: Elements of a synthesis. J. Biogeogr. 35(3), 483–500 (2008).
    Google Scholar 
    Olalla-Tárraga, M. Á. & Rodríguez, M. Á. Energy and interspecific body size patterns of amphibian faunas in Europe and North America: Anurans follow Bergmann’s rule, urodeles its converse. Glob. Ecol. Biogeogr. 16(5), 606–617 (2007).
    Google Scholar 
    Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodríguez, M. Á. Geographic body size gradients in tropical regions: Water deficit and anuran body size in the Brazilian Cerrado. Ecography 32(4), 581–590 (2009).
    Google Scholar 
    Gouveia, S. F. & Correia, I. Geographical clines of body size in terrestrial amphibians: Water conservation hypothesis revisited. J. Biogeogr. 43(10), 2075–2084 (2016).
    Google Scholar 
    Pincheira-Donoso, D., Meiri, S., Jara, M., Olalla-Tárraga, M. Á. & Hodgson, D. J. Global patterns of body size evolution are driven by precipitation in legless amphibians. Ecography 42(10), 1682–1690 (2019).
    Google Scholar 
    Nevo, E. Adaptive color polymorphism in cricket frogs. Evolution 27(3), 353–367 (1973).PubMed 

    Google Scholar 
    Ashton, K. G. Do amphibians follow Bergmann’s rule?. Can. J. Zool. 80(4), 708–716 (2002).MathSciNet 

    Google Scholar 
    Bergmann, C. Ueber die Verhältnisse der Wärmeökonomie der Thiere zu ihrer Grösse. Göttinger Studien. 1, 595–708 (1847).
    Google Scholar 
    Olalla-Tárraga, M. Á., Rodríguez, M. Á. & Hawkins, B. A. Broad-scale patterns of body size in squamate reptiles of Europe and North America. J. Biogeogr. 33(5), 781–793 (2006).
    Google Scholar 
    Trullas, S. C., van Wyk, J. H. & Spotila, J. R. Thermal melanism in ectotherms. J. Therm. Biol. 32(5), 235–245 (2007).
    Google Scholar 
    Rodríguez, M. Á., López-Sañudo, I. L. & Hawkins, B. A. The geographic distribution of mammal body size in Europe. Glob. Ecol. Biogeogr. 15(2), 173–181 (2006).
    Google Scholar 
    Olalla-Tárraga, M. Á., Diniz-Filho, J. A. F., Bastos, R. P. & Rodriguez, M. A. Geographic body size gradients in tropical regions: Water deficit and anuran body size in the Brazilian Cerrado. Ecography 32(4), 581–590 (2009).
    Google Scholar 
    Womack, M. C. & Bell, R. C. Two-hundred million years of anuran body-size evolution in relation to geography, ecology and life history. J. Evol. Biol. 33(10), 1417–1432 (2020).PubMed 

    Google Scholar 
    Frost, D. R. Amphibian Species of the World: An online reference, version 6. http://research.amnh.org/herpetology/amphibia/index.php. Accessed 12 July 2021 (2021).
    Acevedo, A. A., Armesto, O. & Palma, R. E. Two new species of Pristimantis (Anura: Craugastoridae) with notes on the distribution of the genus in northeastern Colombia. Zootaxa 4750(4), 499–523 (2020).
    Google Scholar 
    Heinicke, M. P., Duellman, W. E. & Hedges, S. B. Major Caribbean and Central American frog faunas originated by ancient oceanic dispersal. Proc. Natl. Acad. Sci. USA 104(24), 10092–10097 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Pinto-Sánchez, N. R. et al. The great American biotic interchange in frogs: Multiple and early colonization of Central America by the South American genus Pristimantis (Anura: Craugastoridae). Mol. Phylogenet. Evol. 62(3), 954–972 (2012).PubMed 

    Google Scholar 
    Zumel, D., Buckley, D. & Ron, S. R. The Pristimantis trachyblepharis species group, a clade of miniaturized frogs: Description of four new species and insights into the evolution of body size in the genus. Zool. J. Linn. Soc. zlab044 (2021).Pincheira-Donoso, D. et al. The multiple origins of sexual size dimorphism in global amphibians. Glob. Ecol. Biogeogr. 30(2), 443–458 (2021).
    Google Scholar 
    Woolbright, L. L. Sexual selection and size dimorphism in anuran amphibia. Am. Nat. 121(1), 110–119 (1983).
    Google Scholar 
    Nali, R. C., Zamudio, K. R., Haddad, C. F. & Prado, C. P. Size-dependent selective mechanisms on males and females and the evolution of sexual size dimorphism in frogs. Am. Nat. 184(6), 727–740 (2014).PubMed 

    Google Scholar 
    Hill, R. et al. Herpetological husbandry observations on the captive reproduction of gaige’s rain frog Pristimantis gaigeae (Dunn 1931). Herpetol. Rev. 41(4), 465 (2010).
    Google Scholar 
    Rojas-Rivera, A., Cortés-Bedoya, S., Gutiérrez-Cárdenas, P. D. A. & Castellanos, J. M. Pristimantis achatinus (Cachabi robber frog). Parental care and clutch size. Herpetol. Rev. 42, 588–589 (2011).
    Google Scholar 
    Granados-Pérez, Y. & Ramirez-Pinilla, M. P. Reproductive phenology of three species of Pristimantis in an Andean cloud forest. Revista Acad. Colomb. Ci. Exact. 44(173), 1083–1098 (2020).
    Google Scholar 
    Levy, D. L. & Heald, R. Biological scaling problems and solutions in amphibians. Cold Spring Harb. Perspect. Biol. 8(1), a019166 (2016).PubMed Central 

    Google Scholar 
    O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geol. Survey Data Series. 691(10), 4–9 (2012).
    Google Scholar 
    Valenzuela-Sánchez, A., Cunningham, A. A. & Soto-Azat, C. Geographic body size variation in ectotherms: Effects of seasonality on an anuran from the southern temperate forest. Front. Zool. 12(1), 1–10 (2015).
    Google Scholar 
    Parsons, J. J. The northern Andean environment. Mt. Res. Dev. 2(3), 253–264 (1982).
    Google Scholar 
    Navas, C. A., Carvajalino-Fernández, J. M., Saboyá-Acosta, L. P., Rueda-Solano, L. A. & Carvajalino-Fernández, M. A. The body temperature of active amphibians along a tropical elevation gradient: Patterns of mean and variance and inference from environmental data. Funct. Ecol. 27(5), 1145–1154 (2013).
    Google Scholar 
    Swemmer, A. M., Knapp, A. K. & Snyman, H. A. Intra-seasonal precipitation patterns and above-ground productivity in three perennial grasslands. J. Ecol. 95(4), 780–788 (2007).
    Google Scholar 
    Losos, J. B. Lizards in an Evolutionary Tree: Ecology and Adaptive Radiation of Anoles (Univ. of California Press, 2011).
    Google Scholar 
    Pincheira-Donoso, D. & Hunt, J. Fecundity selection theory: Concepts and evidence. Biol. Rev. 92(1), 341–356 (2017).PubMed 

    Google Scholar 
    Morrison, C. & Hero, J. M. Geographic variation in life-history characteristics of amphibians: A review. J. Anim. Ecol. 72(2), 270–279 (2003).
    Google Scholar 
    Morrow, C. B., Ernest, S. M. & Kerkhoff, A. J. Macroevolution of dimensionless life-history metrics in tetrapods. Proc. Royal Soc. B. 288, 20210200 (2021).
    Google Scholar 
    Revell, L. J., Harmon, L. J. & Collar, D. C. Phylogenetic signal, evolutionary process, and rate. Syst. Biol. 57(4), 591–601 (2008).PubMed 

    Google Scholar 
    Kamilar, J. M. & Cooper, N. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. Lond. B Biol. Sci. 368(1618), 20120341 (2013).PubMed 
    PubMed Central 

    Google Scholar 
    Meyer, A. L. & Wiens, J. J. Estimating diversification rates for higher taxa: BAMM can give problematic estimates of rates and rate shifts. Evolution 72(1), 39–53 (2018).PubMed 

    Google Scholar 
    Rabosky, D. L. et al. Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation. Nat. Commun. 4(1), 1–8 (2013).
    Google Scholar 
    Mendoza, A. M., Ospina, O. E., Cárdenas-Henao, H. & García-R, J. C. A likelihood inference of historical biogeography in the world’s most diverse terrestrial vertebrate genus: Diversification of direct-developing frogs (Craugastoridae: Pristimantis) across the Neotropics. Mol. Phylogenet. Evol. 85, 50–58 (2015).PubMed 

    Google Scholar 
    Baker, J., Meade, A., Pagel, M. & Venditti, C. Adaptive evolution toward larger size in mammals. Proc. Natl. Acad. Sci. USA 112(16), 5093–5098 (2015).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Hariharan, I. K., Wake, D. B. & Wake, M. H. Indeterminate growth: Could it represent the ancestral condition?. Cold Spring Harb. Perspect. Biol. 8(2), a019174 (2016).PubMed Central 

    Google Scholar 
    Amado, T. F., Bidau, C. J. & Olalla-Tárraga, M. Á. Geographic variation of body size in New World anurans: Energy and water in a balance. Ecography 42(3), 456–466 (2019).
    Google Scholar 
    Watters, J. L., Cummings, S. T., Flanagan, R. L. & Siler, C. D. Review of morphometric measurements used in anuran species descriptions and recommendations for a standardized approach. Zootaxa 4072, 477–495 (2016).PubMed 

    Google Scholar 
    Lovich, J. E. & Gibbons, J. W. A review of techniques for quantifying sexual size dimorphism. Growth Dev. Aging. 56, 269–269 (1992).CAS 
    PubMed 

    Google Scholar 
    Lanfear, R., Calcott, B., Ho, S. Y. & Guindon, S. PartitionFinder: Combined selection of partitioning schemes and substitution models for phylogenetic analyses. Mol. Biol. Evol. 29(6), 1695–1701 (2012).CAS 
    PubMed 

    Google Scholar 
    Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7(1), 1–8 (2007).
    Google Scholar 
    Drummond, A. J., Ho, S. Y., Phillips, M. J. & Rambaut, A. Relaxed phylogenetics and dating with confidence. PLoS Biol. 4(5), e88 (2006).PubMed 
    PubMed Central 

    Google Scholar 
    Rambaut, A. FigTree, A Graphical Viewer of Phylogenetic Trees. (2007)Olalla-Tárraga, M. A., Bini, L. M., Diniz-Filho, J. A. & Rodríguez, M. Á. Cross-species and assemblage-based approaches to Bergmann’s rule and the biogeography of body size in Plethodon salamanders of eastern North America. Ecography 33(2), 362–368 (2010).
    Google Scholar 
    QGIS.org. QGIS Geographic Information System. QGIS Association. http://www.qgis.org. Accessed 10 July 2021 (2022).Wei, T. et al. Package ‘corrplot’. Statistician. 56(316), e24 (2017).
    Google Scholar 
    James, F. C. Geographic size variation in birds and its relationship to climate. Ecology 51(3), 365–390 (1970).
    Google Scholar 
    Hawkins, B. A. & Felizola Diniz-Filho, J. A. Beyond Rapoport’s rule: Evaluating range size patterns of New World birds in a two-dimensional framework. Glob. Ecol. Biogeogr. 15(5), 461–469 (2006).
    Google Scholar 
    Eager, C. standardize: Tools for standardizing variables for regression in R. R package version 0.21 (2017).Meireles, J. E., O’Meara, B. & Cavender-Bares, J. Linking leaf spectra to the plant tree of life. In Remote Sensing of Plant Biodiversity (eds Cavender-Bares, J. et al.) 155–172 (Springer, 2010).
    Google Scholar 
    Pagel, M. Inferring the historical patterns of biological evolution. Nature 401(6756), 877–884 (1999).ADS 
    CAS 
    PubMed 

    Google Scholar 
    Pagel, M. The maximum likelihood approach to reconstructing ancestral character states of discrete characters on phylogenies. Syst. Biol. 48(3), 612–622 (1999).
    Google Scholar 
    Revell, L. J. Phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3(2), 217–223 (2012).
    Google Scholar 
    Revell, L. J. Two new graphical methods for mapping trait evolution on phylogenies. Methods Ecol. Evol. 4(8), 754–759 (2013).
    Google Scholar 
    Rabosky, D. L. et al. BAMM tools: An R package for the analysis of evolutionary dynamics on phylogenetic trees. Methods Ecol. Evol. 5(7), 701–707 (2014).
    Google Scholar 
    Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS ONE 9(2), e89543 (2014).ADS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rabosky, D. L., Mitchell, J. S. & Chang, J. Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models. Syst. Biol. 66(4), 477–498 (2017).PubMed 
    PubMed Central 

    Google Scholar 
    Plummer, M., Best, N., Cowles, K. & Vines, K. CODA: Convergence diagnosis and output analysis for MCMC. R News. 6(1), 7–11 (2006).
    Google Scholar 
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2021). https://www.R-project.org. Accessed 1 June 2021 (2021).Fairbairn, D. J. Allometry for sexual size dimorphism: Pattern and process in the coevolution of body size in males and females. Annu. Rev. Ecol. Evol. Syst. 28(1), 659–687 (1997).
    Google Scholar 
    Fairbairn, D. J. Allometry for sexual size dimorphism: Testing two hypotheses for Rensch’s rule in the water strider Aquarius remigis. Am. Nat. 166(S4), S69–S84 (2005).PubMed 

    Google Scholar 
    Visser, A. G., Beevers, L. & Patidar, S. Complexity in hydroecological modelling: A comparison of stepwise selection and information theory. River Res. Appl. 34(8), 1045–1056 (2018).
    Google Scholar 
    Calcagno, V. & de Mazancourt, C. glmulti: An R package for easy automated model selection with (generalized) linear models. J. Stat. Softw. 34(12), 1–29 (2010).
    Google Scholar 
    Callaghan, S., Guilyardi, E., Steenman-Clark, L. & Morgan, M. The METAFOR project. in Earth System Modelling-Volume 1 (Springer, 2013).Garamszegi, L. Z. & Mundry, R. Multimodel-inference in comparative analyses. In Modern Phylogenetic Comparative Methods and THEIR Application in Evolutionary Biology (ed. Garamszegi, L. Z.) 305–331 (Springer Berlin, 2014).
    Google Scholar  More

  • 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

    Intrinsic individual variation in daily activity onset and plastic responses on temporal but not spatial scales in female great tits

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  • 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