in

Physiology can predict animal activity, exploration, and dispersal

  • 1.

    Lihoreau, M. et al. Collective foraging in spatially complex nutritional environments. Philos. Trans. R. Soc. B 372, 20160238–11 (2017).

    Google Scholar 

  • 2.

    Ron, R., Fragman-Sapir, O. & Kadmon, R. Dispersal increases ecological selection by increasing effective community size. Proc. Natl Acad. Sci. USA 115, 11280–11285 (2018).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 3.

    Yeakel, J. D., Gibert, J. P., Gross, T., Westley, P. A. H. & Moore, J. W. Eco-evolutionary dynamics, density-dependent dispersal and collective behaviour: implications for salmon metapopulation robustness. Philos. Trans. R. Soc. B 373, 20170018–13 (2018).

    Google Scholar 

  • 4.

    Baguette, M., Blanchet, S., Legrand, D., Stevens, V. M. & Turlure, C. Individual dispersal, landscape connectivity and ecological networks. Biol. Rev. 88, 310–326 (2013).

    PubMed 

    Google Scholar 

  • 5.

    Schindler, D. E., Armstrong, J. B. & Reed, T. E. The portfolio concept in ecology and evolution. Front. Ecol. Environ. 13, 257–263 (2015).

    Google Scholar 

  • 6.

    McCauley, S. J. & Mabry, K. E. Climate change, body size, and phenotype dependent dispersal. Trends Ecol. Evol. 26, 554–555 (2011).

    PubMed 

    Google Scholar 

  • 7.

    Kerr, J. T. Racing against change: understanding dispersal and persistence to improve species’ conservation prospects. Proc. R. Soc. B 287, 20202061–10 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 8.

    Clobert, J., Galliard, J. L., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).

    PubMed 

    Google Scholar 

  • 9.

    Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225 (2005).

    PubMed 

    Google Scholar 

  • 10.

    Davis, J. M. & Stamps, J. A. The effect of natal experience on habitat preferences. Trends Ecol. Evol. 19, 411–416 (2004).

    PubMed 

    Google Scholar 

  • 11.

    Benard, M. F. & McCauley, S. J. Integrating across life‐history stages: consequences of natal habitat effects on dispersal. Am. Nat. 171, 553–567 (2008).

    PubMed 

    Google Scholar 

  • 12.

    LeRoy, A. & Seebacher, F. Transgenerational effects and acclimation affect dispersal in guppies. Funct. Ecol. 32, 1819–1831 (2018).

    Google Scholar 

  • 13.

    McGhee, K. E., Barbosa, A. J., Bissell, K., Darby, N. A. & Foshee, S. Maternal stress during pregnancy affects activity, exploration and potential dispersal of daughters in an invasive fish. Anim. Behav. 171, 41–50 (2021).

    Google Scholar 

  • 14.

    Yip, E. C., Smith, D. R. & Lubin, Y. Causes of plasticity and consistency of dispersal behaviour in a group-living spider. Anim. Behav. 175, 99–109 (2021).

    Google Scholar 

  • 15.

    Nathan, R. et al. A movement ecology paradigm for unifying organismal movement research. Proc. Natl Acad. Sci. USA 105, 19052–19059 (2008).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 16.

    Hawkes, C. Linking movement behaviour, dispersal and population processes: is individual variation a key? J. Anim. Ecol. 78, 894–906 (2009).

    PubMed 

    Google Scholar 

  • 17.

    Capelli, P., Pivetta, C., Esposito, M. S. & Arber, S. Locomotor speed control circuits in the caudal brainstem. Nature 56, 465–22 (2017).

    Google Scholar 

  • 18.

    Jiang, Y. et al. Sensory trait variation contributes to biased dispersal of threespine stickleback in flowing water. J. Evol. Biol. 30, 681–695 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • 19.

    Malishev, M. & Kramer-Schadt, S. Movement, models, and metabolism: Individual-based energy budget models as next-generation extensions for predicting animal movement outcomes across scales. Ecol. Model. 441, 109413 (2021).

    Google Scholar 

  • 20.

    Klarevas‐Irby, J. A., Wikelski, M. & Farine, D. R. Efficient movement strategies mitigate the energetic cost of dispersal. Ecol. Lett. 24, 1432–1442 (2021).

    PubMed 

    Google Scholar 

  • 21.

    Mathot, K. J., Dingemanse, N. J. & Nakagawa, S. The covariance between metabolic rate and behaviour varies across behaviours and thermal types: meta‐analytic insights. Biol. Rev. 94, 1056–1074 (2019).

    PubMed 

    Google Scholar 

  • 22.

    Killen, S. S., Marras, S., Ryan, M. R., Domenici, P. & McKenzie, D. J. A relationship between metabolic rate and risk-taking behaviour is revealed during hypoxia in juvenile European sea bass. Funct. Ecol. 26, 134–143 (2012).

    Google Scholar 

  • 23.

    Metcalfe, N. B., Leeuwen, T. E. V. & Killen, S. S. Does individual variation in metabolic phenotype predict fish behaviour and performance? J. Fish. Biol. 88, 298–321 (2016).

    CAS 
    PubMed 

    Google Scholar 

  • 24.

    Gordon, A. M., Homsher, E. & Regnier, M. Regulation of contraction in striated muscle. Physiol. Rev. 80, 853–924 (2000).

    CAS 
    PubMed 

    Google Scholar 

  • 25.

    Gundersen, K. Excitation-transcription coupling in skeletal muscle: the molecular pathways of exercise. Biol. Rev. 86, 564–600 (2011).

    PubMed 

    Google Scholar 

  • 26.

    Lichtwark, G. A. & Wilson, A. M. A modified Hill muscle model that predicts muscle power output and efficiency during sinusoidal length changes. J. Exp. Biol. 208, 2831–2843 (2005).

    CAS 
    PubMed 

    Google Scholar 

  • 27.

    Seebacher, F., Tallis, J. A. & James, R. S. The cost of muscle power production: muscle oxygen consumption per unit work increases at low temperatures in Xenopus laevis Daudin. J. Exp. Biol. 217, 1940–1945 (2014).

    PubMed 

    Google Scholar 

  • 28.

    Denton, R. D., Higham, T., Greenwald, K. R. & Gibbs, H. L. Locomotor endurance predicts differences in realized dispersal between sympatric sexual and unisexual salamanders. Funct. Ecol. 31, 915–926 (2017).

    Google Scholar 

  • 29.

    Eliason, E. J. et al. Differences in thermal tolerance among sockeye salmon populations. Science 332, 109–112 (2011).

    CAS 
    PubMed 

    Google Scholar 

  • 30.

    Jahn, M. & Seebacher, F. Cost of transport is a repeatable trait but is not determined by mitochondrial efficiency in zebrafish (Danio rerio). J. Exp. Biol. 222, jeb201400–jeb201407 (2019).

    PubMed 

    Google Scholar 

  • 31.

    Pettersen, A. K., Marshall, D. J. & White, C. R. Understanding variation in metabolic rate. J. Exp. Biol. 221, jeb166876 (2018).

    PubMed 

    Google Scholar 

  • 32.

    Svendsen, J. C., Tirsgaard, B., Cordero, G. A. & Steffensen, J. Intraspecific variation in aerobic and anaerobic locomotion: gilthead sea bream (Sparus aurata) and Trinidadian guppy (Poecilia reticulata) do not exhibit a trade-off between maximum sustained swimming speed and minimum cost of transport. Front. Physiol. 6, 43 (2017).

    Google Scholar 

  • 33.

    Seebacher, F. & Little, A. G. Plasticity of performance curves in ectotherms: individual variation modulates population responses to environmental change. Front. Physiol. 12, 733305 (2021).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 34.

    Freedberg, S., Urban, C. & Cunniff, B. M. Dispersal reduces interspecific competitiveness by spreading locally harmful traits. J. Evol. Biol. 34, 1477–1487 (2021).

    PubMed 

    Google Scholar 

  • 35.

    Ashe, A., Colot, V. & Oldroyd, B. P. How does epigenetics influence the course of evolution? Philos. Trans. R. Soc. B 376, 20200111 (2021).

    CAS 

    Google Scholar 

  • 36.

    Hardie, D. C. & Hutchings, J. A. Evolutionary ecology at the extremes of species ranges. Environ. Rev. 18, 1–20 (2010).

    Google Scholar 

  • 37.

    Charmantier, A., Doutrelant, C., Dubuc‐Messier, G., Fargevieille, A. & Szulkin, M. Mediterranean blue tits as a case study of local adaptation. Evol. Appl. 9, 135–152 (2016).

    PubMed 

    Google Scholar 

  • 38.

    Rohr, J. R. & Cohen, J. M. Understanding how temperature shifts could impact infectious disease. PLoS Biol. 18, e3000938 (2020).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 39.

    Seebacher, F. & Krause, J. Physiological mechanisms underlying animal social behaviour. Philos. Trans. R. Soc. B 372, 20160231–20160238 (2017).

    Google Scholar 

  • 40.

    Avaria-Llautureo, J. et al. Historical warming consistently decreased size, dispersal and speciation rate of fish. Nat. Clim. Change 11, 787–793 (2021).

    Google Scholar 

  • 41.

    Radinger, J. et al. The future distribution of river fish: the complex interplay of climate and land use changes, species dispersal and movement barriers. Glob. Chan. Biol. 23, 4970–4986 (2017).

    Google Scholar 

  • 42.

    Pörtner, H.-O. & Knust, R. Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science 315, 95–97 (2007).

    PubMed 

    Google Scholar 

  • 43.

    Husak, J. F. Measuring selection on physiology in the wild and Manipulating phenotypes (in terrestrial nonhuman vertebrates). Compr. Physiol. 6, 63–85 (2016).

    Google Scholar 

  • 44.

    Hostrup, M. & Bangsbo, J. Limitations in intense exercise performance of athletes—effect of speed endurance training on ion handling and fatigue development. J. Physiol. 595, 2897–2913 (2017).

    CAS 
    PubMed 

    Google Scholar 

  • 45.

    Reale, D. et al. Personality and the emergence of the pace-of-life syndrome concept at the population level. Philos. Trans. R. Soc. B 365, 4051–4063 (2010).

    Google Scholar 

  • 46.

    Auer, S. K. et al. Metabolic rate interacts with resource availability to determine individual variation in microhabitat use in the wild. Am. Nat. 196, 132–144 (2020).

    PubMed 

    Google Scholar 

  • 47.

    Fewell, J. H. & Harrison, J. F. Scaling of work and energy use in social insect colonies. Behav. Ecol. Sociobiol. 70, 1047–1061 (2016).

    Google Scholar 

  • 48.

    LeRoy, A., Mazué, G. P. F., Metcalfe, N. B. & Seebacher, F. Diet and temperature modify the relationship between energy use and ATP production to influence behavior in zebrafish (Danio rerio). Ecol. Evol. 11, 9791–9803 (2021).

    Google Scholar 

  • 49.

    Alcaraz, G. & García-Cabello, K. N. Feeding and metabolic compensations in response to different foraging costs. Hydrobiologia 787, 217–227 (2017).

    Google Scholar 

  • 50.

    Boratyński, Z., Szyrmer, M. & Koteja, P. The metabolic performance predicts home range size of bank voles: a support for the behavioral–bioenergetics theory. Oecologia 193, 547–556 (2020).

    PubMed 

    Google Scholar 

  • 51.

    Killen, S. S., Marras, S., Steffensen, J. F. & McKenzie, D. J. Aerobic capacity influences the spatial position of individuals within fish schools. Proc. R. Soc. B 279, 357–364 (2012).

    PubMed 

    Google Scholar 

  • 52.

    Salin, K. et al. Differences in mitochondrial efficiency explain individual variation in growth performance. Proc. R. Soc. B 286, 20191466–20191468 (2019).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 53.

    Wilson, R. S. & Husak, J. F. Introduction to the symposium: Towards a general framework for predicting animal movement speeds in nature. Integr. Comp. Biol. 55, 1121–1124 (2015).

    PubMed 

    Google Scholar 

  • 54.

    Wheatley, R., Niehaus, A. C., Fisher, D. O. & Wilson, R. S. Ecological context and the probability of mistakes underlie speed choice. Funct. Ecol. 32, 990–1000 (2018).

    Google Scholar 

  • 55.

    Martin, G. R. Understanding bird collisions with man‐made objects: a sensory ecology approach. Ibis 153, 239–254 (2011).

    Google Scholar 

  • 56.

    Husak, J. F. & Fox, S. F. Field use of maximal sprint speed by collared lizards (Crotaphytus collaris): compensation and sexual selection. Evolution 60, 1888–1895 (2006).

    PubMed 

    Google Scholar 

  • 57.

    Mouchet, A. & Dingemanse, N. J. A quantitative genetics approach to validate lab- versus field-based behavior in novel environments. Behav. Ecol. 32, 903–911 (2021).

    Google Scholar 

  • 58.

    O’Connor, E. A., Cornwallis, C. K., Hasselquist, D., Nilsson, J.-Å. & Westerdahl, H. The evolution of immunity in relation to colonization and migration. Nat. Ecol. Evol. 2, 841–849 (2018).

    PubMed 

    Google Scholar 

  • 59.

    Du, J. et al. Dynamic regulation of mitochondrial function by glucocorticoids. Proc. Natl Acad. Sci. USA 106, 3543–3548 (2009).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 60.

    Jaikumar, G., Slabbekoorn, H., Sireeni, J., Schaaf, M. & Tudorache, C. The role of the glucocorticoid receptor in the regulation of diel rhythmicity. Physiol. Behav. 223, 112991 (2020).

    CAS 
    PubMed 

    Google Scholar 

  • 61.

    Castillo-Ramírez, L. A., Ryu, S. & Marco, R. J. D. Active behaviour during early development shapes glucocorticoid reactivity. Sci. Rep. 9, 55–59 (2019).

    Google Scholar 

  • 62.

    Bruijn, Rde & Romero, L. M. The role of glucocorticoids in the vertebrate response to weather. Gen. Comp. Endocrinol. 269, 11–32 (2018).

    PubMed 

    Google Scholar 

  • 63.

    Saastamoinen, M. et al. Genetics of dispersal. Biol. Rev. 93, 574–599 (2018).

    PubMed 

    Google Scholar 

  • 64.

    Seebacher, F., White, C. R. & Franklin, C. E. Physiological plasticity increases resilience of ectothermic animals to climate change. Nat. Clim. Change 5, 61–66 (2015).

    Google Scholar 

  • 65.

    White, C. R. et al. Geographical bias in physiological data limits predictions of global change impacts. Funct. Ecol. 35, 1572–1578 (2021).

    Google Scholar 

  • 66.

    Moher, D. et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst. Rev. 4, 1 (2015).

    PubMed 
    PubMed Central 

    Google Scholar 

  • 67.

    Ouzzani, M., Hammady, H., Fedorowicz, Z. & Elmagarmid, A. Rayyan—a web and mobile app for systematic reviews. Syst. Rev. 5, 1–10 (2016).

    Google Scholar 

  • 68.

    Debeffe, L. et al. Exploration as a key component of natal dispersal: dispersers explore more than philopatric individuals in roe deer. Anim. Behav. 86, 143–151 (2013).

    Google Scholar 

  • 69.

    Careau, V. & T. G., Jr. Performance, personality, and energetics: correlation, causation, and mechanism. Physiol. Biochem. Zool. 85, 543–571 (2012).

    PubMed 

    Google Scholar 

  • 70.

    Chuang, A. & Peterson, C. R. Expanding population edges: theories, traits, and trade‐offs. Glob. Chang. Biol. 22, 494–512 (2016).

    PubMed 

    Google Scholar 

  • 71.

    Arnold, P. A., Delean, S., Cassey, P. & White, C. R. Meta-analysis reveals that resting metabolic rate is not consistently related to fitness and performance in animals. J. Comp. Physiol. B 191, 1097–1110 (2021).

    PubMed 

    Google Scholar 

  • 72.

    Pick, J. L., Nakagawa, S. & Noble, D. W. Reproducible, flexible and high‐throughput data extraction from primary literature: the metaDigitise R package. Method. Ecol. Evol. 10, 426–431 (2019).

    Google Scholar 

  • 73.

    Hedges, L. V. & Olkin, I. Statistical Methods for Meta-Analysis. (Academic Press, 1985).

  • 74.

    Hedges, L. V., Gurevich, J. & Curtis, P. S. The meta‐analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).

    Google Scholar 

  • 75.

    Hinchliff, C. E. et al. Synthesis of phylogeny and taxonomy into a comprehensive tree of life. Proc. Natl Acad. Sci. USA 112, 12764–12769 (2015).

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • 76.

    Michonneau, F., Brown, J. W. & Winter, D. J. rotl: an R package to interact with the Open Tree of Life data. Method. Ecol. Evol. 7, 1476–1481 (2016).

    Google Scholar 

  • 77.

    Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2018).

    Google Scholar 

  • 78.

    Bürkner, P.-C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).

    Google Scholar 

  • 79.

    Bürkner, P. Advanced Bayesian multilevel modeling with the R package brms. R Journal 10, 395–411 (2018).

  • 80.

    Gelman, A. & Rubin, D. B. Inference from iterative simulation using multiple sequences. Stat. Sci. 7, 457–472 (1992).

    Google Scholar 

  • 81.

    Nakagawa, S., Noble, D. W., Senior, A. M. & Lagisz, M. Meta-evaluation of meta-analysis: ten appraisal questions for biologists. BMC Biol. 15, 1–14 (2017).

    Google Scholar 

  • 82.

    Nakagawa, S. et al. Methods for testing publication bias in ecological and evolutionary meta-analyses. Methods Ecol. Evol. (in press, 2021) https://doi.org/10.1111/2041-210X.13724.

  • 83.

    Nakagawa, S. & Santos, E. S. A. Methodological issues and advances in biological meta-analysis. Evol. Ecol. 26, 1253–1274 (2012).

    Google Scholar 

  • 84.

    Wu, N. C. & Seebacher, F. Data for Physiology can predict animal activity, exploration, and dispersal. https://github.com/nicholaswunz/dispersal-meta-analysis.


  • Source: Ecology - nature.com

    Reducing methane emissions at landfills

    Students dive into research with the MIT Climate and Sustainability Consortium