Weather-driven change in primary productivity explains variation in the amplitude of two herbivore population cycles in a boreal system.

Vertebrate populations throughout the circumpolar north often exhibit cyclic dynamics, and predation is generally considered to be a primary driver of these cycles in a variety of herbivore species. However, weather and climate play a role in entraining cycles over broad landscapes and may alter cyclic dynamics, although the mechanism by which these processes operate is uncertain. Experimental and observational work has suggested that weather influences primary productivity over multi-year time periods, suggesting a pathway through which weather and climate may influence cyclic herbivore dynamics. Using long-term monitoring data, we investigated the relationships among multi-year weather conditions, measures of primary productivity, and the abundance of two cyclic herbivore species: snowshoe hare and northern red-backed vole. We found that precipitation (rain and snow) and growing season temperatures were strongly associated with variation in primary productivity over multi-year time horizons. In turn, fourfold variation in the amplitude of both the hare and vole cycles observed in our study area corresponded to long-term changes in primary productivity. The congruence of our results for these two species suggests a general mechanism by which weather and climate might influence cyclic herbivore population dynamics. Our findings also suggested that the association between climate warming and the disappearance of cycles might be initiated by changes in primary productivity. This work provides an explanation for observed influences of weather and climate on primary productivity and population cycles and will help our collective understanding of how future climate warming may influence these ecological phenomena in the future.

Distance software: design and analysis of distance sampling surveys for estimating population size.

1.Distance sampling is a widely used technique for estimating the size or density of biological populations. Many distance sampling designs and most analyses use the software Distance.2.We briefly review distance sampling and its assumptions, outline the history, structure and capabilities of Distance, and provide hints on its use.3.Good survey design is a crucial prerequisite for obtaining reliable results. Distance has a survey design engine, with a built-in geographic information system, that allows properties of different proposed designs to be examined via simulation, and survey plans to be generated.4.A first step in analysis of distance sampling data is modelling the probability of detection. Distance contains three increasingly sophisticated analysis engines for this: conventional distance sampling, which models detection probability as a function of distance from the transect and assumes all objects at zero distance are detected; multiple-covariate distance sampling, which allows covariates in addition to distance; and mark-recapture distance sampling, which relaxes the assumption of certain detection at zero distance.5.All three engines allow estimation of density or abundance, stratified if required, with associated measures of precision calculated either analytically or via the bootstrap.6.Advanced analysis topics covered include the use of multipliers to allow analysis of indirect surveys (such as dung or nest surveys), the density surface modelling analysis engine for spatial and habitat modelling, and information about accessing the analysis engines directly from other software.7.Synthesis and applications. Distance sampling is a key method for producing abundance and density estimates in challenging field conditions. The theory underlying the methods continues to expand to cope with realistic estimation situations. In step with theoretical developments, state-of-the-art software that implements these methods is described that makes the methods accessible to practising ecologists.