Which temporal resolution to consider when investigating the impact of climatic data on population dynamics? The case of the lesser horseshoe bat (Rhinolophus hipposideros)
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Climatic variables are often considered when studying environmental impacts on population dynamics of terrestrial species. However, the temporal resolution considered varies depending on studies, even among studies of the same taxa. Most studies interested in climatic impacts on populations tend to average climatic data across timeframes covering life cycle periods of the organism in question or longer, even though most climatic databases provide at least a monthly resolution. We explored the impact of climatic variables on lesser horseshoe bat (Rhinolophus hipposideros) demography based on count data collected at 94 maternity colonies from 2000 to 2014 in Britanny, France. Meteorological data were considered using different time resolutions (month, life cycle period and year) to investigate their adequacy. Model averaging was used to detect significant predictors for each temporal resolution. Our results show that the finest temporal resolution, e.g. month, was more informative than coarser ones. Precipitation predictors were particularly decisive, with a negative impact on colony sizes when rainfall occurred in October, and a positive impact for June precipitations. Fecundity was influenced by April weather. This highlights the strong impact of climatic conditions during crucial but short time periods on the population dynamics of bats. We demonstrate the importance of choosing an appropriate time resolution and suggest that analogous studies should consider fine-scale temporal resolution (e.g. month) to better grasp the relationship between population dynamics and climatic conditions.
KeywordsRhinolophus hipposideros Temporal resolution Model averaging Climatic variables Population demography
OF and JB provided count data. ELT and AB developed methodology. PLJ, PLG, SJP and EJP analyzed the data. PLJ, PLG, SJP and EJP wrote the manuscript.
- Beltramino AA, Vogler RE, Gutiérrez Gregoric DE, Rumi A (2015) Impact of climate change on the distribution of a giant land snail from South America: predicting future trends for setting conservation priorities on native malacofauna. Clim Change 131:621–633. doi: 10.1007/s10584-015-1405-3 CrossRefGoogle Scholar
- Bontadina F, Arlettaz R, Fankhauser T et al (2000) The lesser horseshoe bat Rhinolophus hipposideros in Switzerland: present status and research recommendations. Le Rhinolophe 14:69–83Google Scholar
- Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, BerlinGoogle Scholar
- Delignette-Muller ML, Dutang C (2014) fitdistrplus: an R package for fitting distributions. J Stat Softw 64:1–34Google Scholar
- Gaisler J (1966) Reproduction in the lesser horseshoe bat (Rhinolophus hipposideros hipposideros Bechstein, 1800). Bijdr Tot Dierkd 36:45–62Google Scholar
- Haysom K, Dekker J, Russ J et al (2013) European bat population trends—a prototype biodiversity indicator. European Environment Agency, DenmarkGoogle Scholar
- Hoyle SD, Pople AR, Toop GJ (2001) Mark–recapture may reveal more about ecology than about population trends: demography of a threatened ghost bat (Macroderma gigas) population. Austral Ecol 26:80–92Google Scholar
- Lamy C, Dubreuil V (2010) Impact des sécheresses en bretagne sur le bilan hydrique: modélisation à partir du climat d’années passées—23ème Colloque de l’Association Internationale de Climatologie, pp 325–330Google Scholar
- Nouvellet P, Newman C, Buesching CD, Macdonald DW (2013) A multi-metric approach to investigate the effects of weather conditions on the demographic of a terrestrial mammal, the european badger (Meles meles). PLoS One 8:e68116. doi: 10.1371/journal.pone.0068116 CrossRefPubMedPubMedCentralGoogle Scholar
- Reiter G (2004b) The importance of woodland for Rhinolophus hipposideros (Chiroptera, Rhinolophidae) in Austria. Mamm Mamm 68:403–410Google Scholar
- Van der Wal J, Beaumont L, Zimmerman N, Lorch P (2014) Climates: methods for working with weather and climate. R package version 0.1–1.6Google Scholar