Habitat foraging niche of a High Arctic zooplanktivorous seabird in a changing environment

Abstract Here, we model current and future distribution of a foraging Arctic endemic species, the little auk (Alle alle), a small zooplanktivorous Arctic seabird. We characterized environmental conditions [sea depth, sea surface temperature (SST), marginal sea ice zone (MIZ)] at foraging positions of GPS-tracked individuals from three breeding colonies in Svalbard: one located at the southern rim of the Arctic zone (hereafter ‘boreo-Arctic’) and two in the high-Arctic zone on Spitsbergen (‘high-Arctic’). The birds from one ‘high-Arctic’ colony, influenced by cold Arctic water, foraged in the shallow shelf zone near the colony. The birds from remaining colonies foraged in a wider range of depths, in a higher SST zone (‘boreo-Arctic’) or in the productive but distant MIZ (second ‘high-Arctic’ colony). Given this flexible foraging behaviour, little auks may be temporarily resilient to moderate climate changes. However, our fuzzy logic models of future distribution under scenarios of 1 °C and 2 °C SST increase predict losses of suitable foraging habitat for the majority of little auk colonies studied. Over longer time scales negative consequences of global warming are inevitable. The actual response of little auks to future environmental conditions will depend on the range of their plasticity and pace of ecosystem changes

Econometric Forecasting

Several principles are useful for econometric forecasters: keep the model simple, use all the data you can get, and use theory (not the data) as a guide to selecting causal variables. But theory gives little guidance on dynamics, that is, on which lagged values of the selected variables to use. Early econometric models failed in comparison with extrapolative methods because they paid too little attention to dynamic structure. In a fairly simple way, the vector autoregression (VAR) approach that first appeared in the 1980s resolved the problem by shifting emphasis towards dynamics and away from collecting many causal variables. The VAR approach also resolves the question of how to make long-term forecasts where the causal variables themselves must be forecast. When the analyst does not need to forecast causal variables or can use other sources, he or she can use a single equation with the same dynamic structure. Ordinary least squares is a perfectly adequate estimation method. Evidence supports estimating the initial equation in levels, whether the variables are stationary or not. We recommend a general-to-specific model-building strategy: start with a large number of lags in the initial estimation, although simplifying by reducing the number of lags pays off. Evidence on the value of further simplification is mixed. If cointegration among variables, then error-correction models (ECMs) will do worse than equations in levels. But ECMs are only sometimes an improvement eve