in

Climate change and anthropogenic food manipulation interact in shifting the distribution of a large herbivore at its altitudinal range limit

[adace-ad id="91168"]
  • 1.

    Weiner, J. Physiological limits to sustainable energy budgets in birds and mammals: ecological implications. Trends Ecol. Evol. 7, 384–388 (1992).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 2.

    Mcnab, B. K. Food habits, energetics, and the population biology of mammals. Am. Nat. 116, 106–124 (1980).

    Article 

    Google Scholar 

  • 3.

    Hovey, F. W. & Harestad, A. S. Estimating effects of snow on shrub availability for black-tailed deer in southwestern British Columbia. Wildl. Soc. Bull. 20, 308–313 (1992).

    Google Scholar 

  • 4.

    Post, E. & Stenseth, N. Climatic variability, plant phenology, and northern ungulates. Ecology 80, 1322–1339 (1999).

    Article 

    Google Scholar 

  • 5.

    Moen, A. N. Seasonal changes in heart rates, activity, metabolism, and forage intake of white-tailed deer. J. Wildl. Manag. 42, 715–738 (1978).

    Article 

    Google Scholar 

  • 6.

    Holand, Ø., Mysterud, A., Wannag, A. & Linnell, J. D. C. Roe deer in northern environments: physiology and behaviour. In The European Roe Deer: Biology of Success (eds Andersen, R. et al.) 117–137 (Scandinavian University Press, 1998).

    Google Scholar 

  • 7.

    Foromozov, A. N. Snow Cover as an Integral Factor of the Environment and Its Importance in the Ecology of Mammals and Birds (The University of Alberta, 1963).

    Google Scholar 

  • 8.

    Cagnacci, F. et al. Partial migration in roe deer: migratory and resident tactics are end points of a behavioural gradient determined by ecological factors. Oikos 120, 1790–1802 (2011).

    Article 

    Google Scholar 

  • 9.

    Dussault, C., Courtois, R., Ouellet, J.-P. & Girard, I. Space use of moose in relation to food availability. Can. J. Zool. 83, 1431–1437 (2005).

    Article 

    Google Scholar 

  • 10.

    Mysterud, A. & Sæther, B.-E. Climate change and implications for the future distribution and management of ungulates in Europe. In Ungulate Management in Europe: Problems and Practices (eds Putman, R. et al.) 349–375 (Cambridge University Press, 2011).

    Google Scholar 

  • 11.

    Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669 (2006).

    Article 

    Google Scholar 

  • 12.

    Scherrer, S. C., Wüthrich, C., Croci-Maspoli, M., Weingartner, R. & Appenzeller, C. Snow variability in the Swiss Alps 1864–2009. Int. J. Climatol. 33, 3162–3173 (2013).

    Article 

    Google Scholar 

  • 13.

    Milner, J. M., van Beest, F. M., Schmidt, K. T., Brook, R. K. & Storaas, T. To feed or not to feed? Evidence of the intended and unintended effects of feeding wild ungulates. J. Wildl. Manag. 78, 1322–1334 (2014).

    Article 

    Google Scholar 

  • 14.

    Ossi, F. et al. Plastic response by a small cervid to supplemental feeding in winter across a wide environmental gradient. Ecosphere 8, e01629 (2017).

    Article 

    Google Scholar 

  • 15.

    Putman, R. & Staines, B. W. Supplementary winter feeding of wild red deer Cervus elaphus in Europe and North America: justifications, feeding practice and effectiveness. Mamm. Rev. 34, 285–306 (2004).

    Article 

    Google Scholar 

  • 16.

    Cagnacci, F., Boitani, L., Powell, R. A. & Boyce, M. S. Animal ecology meets GPS-based radiotelemetry: a perfect storm of opportunities and challenges. Philos. Trans. R. Soc. B Biol. Sci. 365, 2157–2162 (2010).

    Article 

    Google Scholar 

  • 17.

    Peters, W. et al. Migration in geographic and ecological space by a large herbivore. Ecol. Monogr. 87, 297–320 (2017).

    Article 

    Google Scholar 

  • 18.

    Morellet, N. et al. Seasonality, weather and climate affect home range size in roe deer across a wide latitudinal gradient within Europe. J. Anim. Ecol. 82, 1326–1339 (2013).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 19.

    Johnson, D. H. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61, 65–71 (1980).

    Article 

    Google Scholar 

  • 20.

    Ossi, F., Gaillard, J. M., Hebblewhite, M. & Cagnacci, F. Snow sinking depth and forest canopy drive winter resource selection more than supplemental feeding in an alpine population of roe deer. Eur. J. Wildl. Res. 61, 111–124 (2015).

    Article 

    Google Scholar 

  • 21.

    Mysterud, A. & Østbye, E. Bed-site selection by European roe deer (Capreolus capreolus) in southern Norway during winter. Can. J. Zool. 73, 924–932 (1995).

    Article 

    Google Scholar 

  • 22.

    Ramanzin, M., Sturaro, E. & Zanon, D. Seasonal migration and home range of roe deer (Capreolus capreolus) in the Italian eastern Alps. Can. J. Zool. 85, 280–289 (2007).

    Article 

    Google Scholar 

  • 23.

    Endrizzi, S., Gruber, S., Dall’Amico, M. & Rigon, R. GEOtop 2.0: simulating the combined energy and water balance at and below the land surface accounting for soil freezing, snow cover and terrain effects. Geosci. Model. Dev. 7, 2831–2857 (2014).

    Article 
    ADS 

    Google Scholar 

  • 24.

    Cohen, J. A coefficient of agreement for nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960).

    Article 

    Google Scholar 

  • 25.

    Thomson, A. M. et al. RCP 4.5: a pathway for stabilization of radiative forcing by 2100. Clim. Change 109, 77–94 (2011).

    CAS 
    Article 
    ADS 

    Google Scholar 

  • 26.

    Riahi, K. et al. RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim. Change 109, 33–57 (2011).

    CAS 
    Article 
    ADS 

    Google Scholar 

  • 27.

    Thomas, C. D. Climate, climate change and range boundaries. Divers. Distrib. 16, 488–495 (2010).

    Article 

    Google Scholar 

  • 28.

    Penteriani, V. et al. Evolutionary and ecological traps for brown bears Ursus arctos in human-modified landscapes. Mamm. Rev. 48, 180–193 (2018).

    Article 

    Google Scholar 

  • 29.

    Sorensen, A., van Beest, F. M. & Brook, R. K. Impacts of wildlife baiting and supplemental feeding on infectious disease transmission risk: a synthesis of knowledge. Prev. Vet. Med. 113, 356–363 (2014).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 30.

    Mysterud, A., Viljugrein, H., Solberg, E. J. & Rolandsen, C. M. Legal regulation of supplementary cervid feeding facing chronic wasting disease. J. Wildl. Manag. 83, 1667–1675 (2019).

    Article 

    Google Scholar 

  • 31.

    Ceacero, F. et al. Benefits for dominant red deer hinds under a competitive feeding system: food access behavior, diet and nutrient selection. PLoS ONE 7, e32780 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 

  • 32.

    Beever, E. A. et al. Behavioral flexibility as a mechanism for coping with climate change. Front. Ecol. Environ. 15, 299–308 (2017).

    Article 

    Google Scholar 

  • 33.

    Loe, L. E. et al. Behavioral buffering of extreme weather events in a high-Arctic herbivore. Ecosphere 7, e01374 (2016).

    Article 

    Google Scholar 

  • 34.

    Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • 35.

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

    PubMed 
    PubMed Central 
    Article 
    ADS 
    CAS 

    Google Scholar 

  • 36.

    Mysterud, A. Still walking on the wild side? Management actions as steps towards ‘semi-domestication’ of hunted ungulates. J. Appl. Ecol. 47, 920–925 (2010).

    Article 

    Google Scholar 

  • 37.

    Felton, A. M. et al. Interactions between ungulates, forests, and supplementary feeding: the role of nutritional balancing in determining outcomes. Mamm. Res. 62, 1–7 (2017).

    Article 

    Google Scholar 

  • 38.

    Ricci, S. et al. Impact of supplemental winter feeding on ruminal microbiota of roe deer Capreolus capreolus. Wildl. Biol. 2019, wlb.00572 (2019).

    Article 

    Google Scholar 

  • 39.

    Lone, K. et al. Living and dying in a multi-predator landscape of fear: roe deer are squeezed by contrasting pattern of predation risk imposed by lynx and humans. Oikos 123, 641–651 (2014).

    Article 

    Google Scholar 

  • 40.

    Chapron, G. et al. Recovery of large carnivores in Europe’s modern human-dominated landscapes. Science (80-) 346, 1517–1519 (2014).

    CAS 
    Article 
    ADS 

    Google Scholar 

  • 41.

    Milanesi, P., Breiner, F. T., Puopolo, F. & Holderegger, R. European human-dominated landscapes provide ample space for the recolonization of large carnivore populations under future land change scenarios. Ecography (Cop.) 40, 1359–1368 (2017).

    Article 

    Google Scholar 

  • 42.

    Pascual-Rico, R. et al. Is diversionary feeding a useful tool to avoid human-ungulate conflicts? A case study with the aoudad. Eur. J. Wildl. Res. 64, 1–7 (2018).

    Article 

    Google Scholar 

  • 43.

    van Beest, F. M., Loe, L. E., Mysterud, A. & Milner, J. M. Comparative space use and habitat selection of moose around feeding stations. J. Wildl. Manag. 74, 219–227 (2010).

    Article 

    Google Scholar 

  • 44.

    Jerina, K. Roads and supplemental feeding affect home-range size of Slovenian red deer more than natural factors. J. Mamm. 93, 1139–1148 (2012).

    Article 

    Google Scholar 

  • 45.

    Ranc, N. et al. Preference and familiarity mediate spatial responses of a large herbivore to experimental manipulation of resource availability. Scientific Reports 10, 11946 (2020). 

  • 46.

    Brown, R. D. & Robinson, D. A. Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty. Cryosphere 5, 219–229 (2011).

    Article 
    ADS 

    Google Scholar 

  • 47.

    Schloss, C. A., Nuñez, T. A. & Lawler, J. J. Dispersal will limit ability of mammals to track climate change in the Western Hemisphere. Proc. Natl. Acad. Sci. U. S. A. 109, 8606–8611 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 
    ADS 

    Google Scholar 

  • 48.

    Gurarie, E. et al. A framework for modelling range shifts and migrations: asking when, whither, whether and will it return. J. Anim. Ecol. 86, 943–959 (2017).

    PubMed 
    Article 

    Google Scholar 

  • 49.

    Rivrud, I. M. et al. Leave before it’s too late: anthropogenic and environmental triggers of autumn migration in a hunted ungulate population. Ecology 97, 1058–1065 (2016).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 50.

    Courtois, R., Dussault, C., Potvin, F. & Daigle, G. Habitat selection by moose (Alces alces) in clear-cut landscapes. Alces 38, 177–192 (2002).

    Google Scholar 

  • 51.

    Gilbert, S. L., Hundertmark, K. J., Person, D. K., Lindberg, M. S. & Boyce, M. S. Behavioral plasticity in a variable environment: snow depth and habitat interactions drive deer movement in winter. J. Mamm. 98, 246–259 (2017).

    Article 

    Google Scholar 

  • 52.

    Chevin, L. M., Lande, R. & Mace, G. M. Adaptation, plasticity, and extinction in a changing environment: towards a predictive theory. PLoS Biol. 8, e1000357 (2010).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • 53.

    Bauer, S. & Hoye, B. J. Migratory animals couple biodiversity and ecosystem functioning worldwide. Science (80-) 344, 1242552 (2014).

    CAS 
    Article 

    Google Scholar 

  • 54.

    Mason, T. H. E., Stephens, P. A., Apollonio, M. & Willis, S. G. Predicting potential responses to future climate in an alpine ungulate: Interspecific interactions exceed climate effects. Glob. Change Biol. 20, 3872–3882 (2014).

    Article 
    ADS 

    Google Scholar 

  • 55.

    Carnevali, L., Pedrotti, L., Riga, F. & Toso, S. Banca dati ungulati: Status, distribuzione, consistenza, gestione e prelievo venatorio delle popolazioni di ungulati in Italia. Rapporto 2001–2005 Vol. 117 (Biologia e Conservazione della Fauna, 2009).

    Google Scholar 

  • 56.

    Provincia Autonoma di Trento. Analisi delle consistenze e dei prelievi di ungulati, tetraonidi e coturnice. Stagione Venatoria 2018 (Provincia Autonoma di Trento, 2018).

    Google Scholar 

  • 57.

    Rockel, B., Will, A. & Hense, A. The regional climate model COSMO-CLM (CCLM). Meteorol. Z. 17, 347–348 (2008).

    Article 

    Google Scholar 

  • 58.

    Boyce, M. S. & McDonald, L. L. Relating populations to habitats using resource selection functions. Trends Ecol. Evol. 14, 268–272 (1999).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 59.

    Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Modell. 157, 281–300 (2002).

    Article 

    Google Scholar 

  • 60.

    Benoit, T. & Achraf, E. suncalc: compute sun position, sunlight phases, moon position and lunar phase. R package version 0.5.0. https://cran.r-project.org/package=suncalc (2019).

  • 61.

    DeCesare, N. J. et al. Transcending scale dependece in identifying habitat with resource selection functions. Ecol. Appl. 22, 1068–1083 (2012).

    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 62.

    Kendall, M. A new measure of rank correlation. Biometrika 30, 81–89 (1938).

    MATH 
    Article 

    Google Scholar 

  • 63.

    Cohen, J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol. Bull. 70, 213–220 (1968).

    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 

  • 64.

    Gamer, M., Lemon, J., Fellows, I. & Singh, P. irr: various coefficients of interrater reliability and agreement. R package version 0.84.1. https://cran.r-project.org/package=irr (2019).

  • 65.

    Lele, S. R., Keim, J. L. & Solymos, P. ResourceSelection: resource selection (probability) functions for use-availability data. R package version 0.3-5. https://cran.r-project.org/package=ResourceSelection (2019).

  • 66.

    Bivand, R., Keitt, T. & Rowlingson, B. rgdal: bindings for the ‘Geospatial’ Data Abstraction Library. R package version 1.4-8. https://cran.r-project.org/package=rgdal (2019).

  • 67.

    McLeod, A. I. Kendall: Kendall rank correlation and Mann-Kendall trend test. R package version 2.2. https://cran.r-project.org/package=Kendall (2011).

  • 68.

    Bright Ross, J. G., Peters, W., Ossi, F., Moorcroft P. R., Cordano, E., Eccel, E., Bianchini, F., Ramanzin, M., and Cagnacci, F. Datasets for “Climate change and anthropogenic food manipulation interact in shifting the distribution of a large herbivore at its altitudinal range limit.” https://doi.org/10.5281/zenodo.4637674 (2021).


  • Source: Ecology - nature.com

    Author Correction: Detection of untreated sewage discharges to watercourses using machine learning

    Genetic diversity and population structure of razor clam Sinonovacula constricta in Ariake Bay, Japan, revealed using RAD-Seq SNP markers