Preprint

Global urban environmental change drives adaptation in white clover

Authors:
  • University of Toronto @ Mississauga, Mississauga, Canada
Preprints and early-stage research may not have been peer reviewed yet.
To read the file of this research, you can request a copy directly from the authors.

No file available

Request Full-text Paper PDF

To read the file of this research,
you can request a copy directly from the authors.

ResearchGate has not been able to resolve any citations for this publication.
Full-text available
Article
Data from remote sensing are often used as proxies to quantify biological processes, especially at large geographical scales. The normalized difference vegetation index (NDVI) is the most frequently used proxy for primary productivity. Assuming a direct, positive interrelation between primary and secondary production in terrestrial habitats, NDVI is often used to predict food availability for higher trophic levels. However, the relationship between NDVI and arthropod biomass has rarely been tested. In this study, we analyzed extensive datasets of arthropod communities from semi-natural grasslands in central Europe to test the relationship between arthropod biomass of consumer trophic levels ("herbivores," "mixed," and "carnivores") in grassland communities and NDVI during the spring season. We found that arthropod biomass generally increased with NDVI. The same positive relationship between biomass and NDVI was observed for each individual trophic level. Cross-correlation analyses did not show statistically significant lags between the NDVI and biomass of herbivores and carnivores. All in all, our study provides correlational evidence for the positive relation of primary and secondary productivity in temperate terrestrial habitats during spring. Moreover, it supports the applicability of NDVI data as a suitable habitat-specific proxy for the food availability of insectivores during spring.
Full-text available
Article
In general, individuals who survive to reproduce have genotypes that work relatively well under local conditions. Migrating or dispersing offspring elsewhere is likely to decrease an individual's or its offspring's fitness, not to mention the intrinsic costs and risks of dispersal. Gene flow into a population can counteract gene frequency changes because of selection, imposing a limit on local adaptation. In addition, the migrant flow tends to be higher from densely populated to sparsely populated areas. Thus, although the potential for adaptation might be greatest in poor and sparsely populated environments, gene flow will counteract selection more strongly in such populations. Recent papers, both theoretical and empirical, have clarified the important role of migration in evolution, affecting spatial patterns, species ranges and adaptation to the environment; in particular, by emphasizing the crucial interaction between evolutionary and demographic processes.
  • N B Grimm
N. B. Grimm et al., Science 319, 756-760 (2008).
  • M T J Johnson
  • J Munshi-South
M. T. J. Johnson, J. Munshi-South, Science 358, eaam8327 (2017).
  • L S Miles
  • L R Rivkin
  • M T J Johnson
  • J Munshi-South
  • B C Verrelli
L. S. Miles, L. R. Rivkin, M. T. J. Johnson, J. Munshi-South, B. C. Verrelli, Mol. Ecol. 28, 4138-4151 (2019).
  • C Schmidt
  • M Domaratzki
  • R P Kinnunen
  • J Bowman
  • C J Garroway
C. Schmidt, M. Domaratzki, R. P. Kinnunen, J. Bowman, C. J. Garroway, Proc. Biol. Sci. 287, 20192497 (2020).
  • E M Oziolor
E. M. Oziolor et al., Science 364, 455-457 (2019).
  • K M Winchell
  • R G Reynolds
  • S R Prado-Irwin
  • A R Puente-Rolón
K. M. Winchell, R. G. Reynolds, S. R. Prado-Irwin, A. R. Puente-Rolón, L. J. Revell, Evolution 70, 1009-1022 (2016).
  • L R Rivkin
L. R. Rivkin et al., Evol. Appl. 12, 384-398 (2019).
  • M R Lambert
  • C M Donihue
M. R. Lambert, C. M. Donihue, Nat. Ecol. Evol. 4, 903-910 (2020).
  • M Alberti
M. Alberti, Trends Ecol. Evol. 30, 114-126 (2015).
  • C J Schell
C. J. Schell et al., Science 369, eaay4497 (2020).
  • D I Bolnick
  • R D Barrett
  • K B Oke
  • D J Rennison
  • Y E Stuart
D. I. Bolnick, R. D. Barrett, K. B. Oke, D. J. Rennison, Y. E. Stuart, Annu. Rev. Ecol. Evol. Syst. 49, 303-330 (2018).
  • J B Losos
J. B. Losos, Evolution 65, 1827-1840 (2011).
  • P M Groffman
P. M. Groffman et al., Front. Ecol. Environ. 12, 74-81 (2014).
  • K M Olsen
  • L L Small
K. M. Olsen, L. L. Small, New Phytol. 219, 757-766 (2018).
  • K M Olsen
  • B L Sutherland
  • L L Small
K. M. Olsen, B. L. Sutherland, L. L. Small, Mol. Ecol. 16, 4180-4193 (2007).
  • N J Kooyers
  • K M Olsen
N. J. Kooyers, K. M. Olsen, J. Evol. Biol. 27, 2554-2558 (2014).
  • N J Kooyers
  • B Hartman Bakken
  • M C Ungerer
  • K M Olsen
N. J. Kooyers, B. Hartman Bakken, M. C. Ungerer, K. M. Olsen, Am. J. Bot. 105, 1224-1231 (2018).
  • M Hughes
M. Hughes, Heredity 66, 105-115 (1991).
  • N J Kooyers
  • L R Gage
  • A Al-Lozi
  • K M Olsen
N. J. Kooyers, L. R. Gage, A. Al-Lozi, K. M. Olsen, Mol. Ecol. 23, 1053-1070 (2014).
  • H Daday
H. Daday, Heredity 12, 169-184 (1958).
  • M T J Johnson
  • C M Prashad
  • M Lavoignat
  • H S Saini
M. T. J. Johnson, C. M. Prashad, M. Lavoignat, H. S. Saini, Proc. Biol. Sci. 285, 20181019 (2018).
  • K A Thompson
  • M Renaudin
  • M T J Johnson
K. A. Thompson, M. Renaudin, M. T. J. Johnson, Proc. Biol. Sci. 283, 20162180 (2016).
  • J S Santangelo
J. S. Santangelo et al., Evol. Lett. 4, 212-225 (2020).
  • J S Santangelo
  • M T J Johnson
  • R W Ness
J. S. Santangelo, M. T. J. Johnson, R. W. Ness, Proc. Biol. Sci. 285, 20180230 (2018).
  • N J Kooyers
  • K M Olsen
N. J. Kooyers, K. M. Olsen, Heredity 111, 495-504 (2013).
  • L S Miles
  • S T Breitbart
  • H H Wagner
  • M T J Johnson
L. S. Miles, S. T. Breitbart, H. H. Wagner, M. T. J. Johnson, Front. Ecol. Evol. 7, 310 (2019).