Detecting failure of climate predictions

The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty. Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies. Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055

Long-term trends in survival of a declining population: the case of the little owl (Athene noctua) in the Netherlands

The little owl (Athene noctua) has declined significantly in many parts of Europe, including the Netherlands. To understand the demographic mechanisms underlying their decline, we analysed all available Dutch little owl ringing data. The data set spanned 35 years, and included more than 24,000 ringed owls, allowing detailed estimation of survival rates through multi-state capture–recapture modelling taking dispersal into account. We investigated geographical and temporal variation in age-specific survival rates and linked annual survival estimates to population growth rate in corresponding years, as well as to environmental covariates. The best model for estimating survival assumed time effects on both juvenile and adult survival rates, with average annual survival estimated at 0.258 (SE = 0.047) and 0.753 (SE = 0.019), respectively. Juvenile survival rates decreased with time whereas adult survival rates fluctuated regularly among years, low survival occurring about every 4 years. Years when the population declined were associated with low juvenile survival. More than 60% of the variation in juvenile survival was explained by the increase in road traffic intensity or in average temperature in spring, but these correlations rather reflect a gradual decrease in juvenile survival coinciding with long-term global change than direct causal effects. Surprisingly, vole dynamics did not explain the cyclic dynamics of adult survival rate. Instead, dry and cold years led to low adult survival rates. Low juvenile survival rates, that limit recruitment of first-year breeders, and the regular occurrence of years with poor adult survival, were the most important determinants of the population decline of the little owl

Long-term trends in survival of a declining population: the case of the little owl (Athene noctua) in the Netherlands

The little owl (Athene noctua) has declined significantly in many parts of Europe, including the Netherlands. To understand the demographic mechanisms underlying their decline, we analysed all available Dutch little owl ringing data. The data set spanned 35 years, and included more than 24,000 ringed owls, allowing detailed estimation of survival rates through multi-state capture–recapture modelling taking dispersal into account. We investigated geographical and temporal variation in age-specific survival rates and linked annual survival estimates to population growth rate in corresponding years, as well as to environmental covariates. The best model for estimating survival assumed time effects on both juvenile and adult survival rates, with average annual survival estimated at 0.258 (SE = 0.047) and 0.753 (SE = 0.019), respectively. Juvenile survival rates decreased with time whereas adult survival rates fluctuated regularly among years, low survival occurring about every 4 years. Years when the population declined were associated with low juvenile survival. More than 60% of the variation in juvenile survival was explained by the increase in road traffic intensity or in average temperature in spring, but these correlations rather reflect a gradual decrease in juvenile survival coinciding with long-term global change than direct causal effects. Surprisingly, vole dynamics did not explain the cyclic dynamics of adult survival rate. Instead, dry and cold years led to low adult survival rates. Low juvenile survival rates, that limit recruitment of first-year breeders, and the regular occurrence of years with poor adult survival, were the most important determinants of the population decline of the little owl