Article ID: | iaor20013384 |
Country: | Netherlands |
Volume: | 134 |
Start Page Number: | 73 |
End Page Number: | 87 |
Publication Date: | Jan 2000 |
Journal: | Ecological Modelling |
Authors: | Kangas A., Kurki S. |
Keywords: | biology |
Risk analysis is increasingly used in assessing the future of rare and vulnerable species. Often, the probability of the extinction of a population in certain time horizon is assessed. Risk analyses are based on a stochastic model of population dynamics. The assumptions about the population model and the values of parameters are crucial in risk analysis: small changes in model assumptions or parameter values can lead to markedly different results. Thus, the results of risk analysis should be interpreted very carefully. However, even if the results are uncertain, they can provide useful insight for the population dynamics and also reveal the possible gaps in the knowledge of species ecology. In this paper, the future of capercaillie in different regions in Finland was assessed with risk analysis. The risk analysis was carried out using Bayesian population dynamics modeling. In the analyses, Finnish wildlife triangle census data (1989–1997) were utilized and annual density, breeding success and adult sex ratio were computed for each study region from the data. Because the annual mortality rates cannot be directly measured from the wildlife triangle data, the analysis was performed with two different assumptions of the mortality rates for adult and juvenile birds: (1) the mortality rates are density dependent; and (2) mortality rates are not related to density. The results of risk analysis with these two assumptions differed considerably. If the mortality is strictly density dependent, the future of capercaillie seems secure, whereas in other case the future seems quite difficult. To be able to produce more reliable estimates about the future of capercaillie in Finland more detailed information about regional and annual variation in mortality rates as well as the pattern of density dependence of mortality rates are needed.