The relative roles of density and climatic variation on population dynamics and fecundity rates in three contrasting ungulate species

Citation
T. Coulson et al., The relative roles of density and climatic variation on population dynamics and fecundity rates in three contrasting ungulate species, P ROY SOC B, 267(1454), 2000, pp. 1771-1779
Citations number
47
Categorie Soggetti
Experimental Biology
Journal title
PROCEEDINGS OF THE ROYAL SOCIETY OF LONDON SERIES B-BIOLOGICAL SCIENCES
ISSN journal
09628452 → ACNP
Volume
267
Issue
1454
Year of publication
2000
Pages
1771 - 1779
Database
ISI
SICI code
0962-8452(20000907)267:1454<1771:TRRODA>2.0.ZU;2-C
Abstract
The relative influences of density-dependent and -independent processes on vital rates and population dynamics have been debated in ecology for over h alf a century, yet it is only recently that both processes have been shown to operate within the same population. However, generalizations on the role of each process across species are rare. Using a process-orientated genera lized linear modelling approach we show that variations in fecundity rates in populations of three species of ungulates with contrasting life historie s are associated with density and winter weather in a remarkably similar ma nner. However, there are differences and we speculate that they are a resul t of differences in size between the species. Much previous research explor ing the association between vital rates, population dynamics and density-de pendent and -independent processes has used pattern-orientated approaches t o decompose time-series into contributions from density-dependent and -inde pendent processes. Results from these analyses are sometimes used to infer associations between vital rates, density and climatic variables. We compar e results from pattern-orientated analyses of time-series with process-orie ntated analyses and report that the two approaches give different results. The approach of analysing relationships between vital rates, density and cl imatic variables may detect important processes influencing population dyna mics that time-series methodologies may overlook.