Geographical gradients in the population dynamics of North American prairie ducks

Weather is one of the most basic factors impacting animal populations, but the typical strength of such impacts on population dynamics is unknown. We incorporate weather and climate index data into analysis of 492 time series of mammals, birds and insects from the global population dynamics database. A conundrum is that a multitude of weather data may a priori be considered potentially important and hence present a risk of statistical over-fitting. We find that model selection or averaging alone could spuriously indicate that weather provides strong improvements to short-term population prediction accuracy. However, a block randomization test reveals that most improvements result from over-fitting. Weather and climate variables do, in general, improve predictions, but improvements were barely detectable despite the large number of datasets considered. Climate indices such as North Atlantic Oscillation are not better predictors of population change than local weather variables. Insect time series are typically less predictable than bird or mammal time series, although all taxonomic classes display low predictability. Our results are in line with the view that population dynamics is often too complex to allow resolving mechanisms from time series, but we argue that time series analysis can still be useful for estimating net environmental effects.

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“…[6][7][8][9][10]). Apart from the potential problem of complexity, weather is often just an indirect cause of population change.…”

Section: Introductionmentioning

confidence: 99%

Effects of weather and climate on the dynamics of animal population time series

2010

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“…In one such group, North American waterfowl, density dependence in population growth has been demonstrated consistently at large spatial and temporal scales (Vickery and Nudds 1984;Viljugrein et al 2005;Saether et al 2008;Murray et al 2010), yet the underlying processes that produce these patterns remain elusive. Waterfowl populations are often limited by productivity on the breeding grounds (Hoekman et al 2002), and nest predation is the primary cause of reproductive failure (Klett et al 1988;Greenwood et al 1995).…”

Section: Introductionmentioning

confidence: 99%

Density-dependent nest predation in waterfowl: the relative importance of nest density versus nest dispersion

2011

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When nest predation levels are very high or very low, the absolute range of observable nest success is constrained (a floor/ceiling effect), and it may be more difficult to detect density-dependent nest predation. Density-dependent nest predation may be more detectable in years with moderate predation rates, simply because there can be a greater absolute difference in nest success between sites. To test this, we replicated a predation experiment 10 years after the original study, using both natural and artificial nests, comparing a year when overall rates of nest predation were high (2000) to a year with moderate nest predation (2010). We found no evidence for density-dependent predation on artificial nests in either year, indicating that nest predation is not density-dependent at the spatial scale of our experimental replicates (1-ha patches). Using nearest-neighbor distances as a measure of nest dispersion, we also found little evidence for "dispersion-dependent" predation on artificial nests. However, when we tested for dispersion-dependent predation using natural nests, we found that nest survival increased with shorter nearest-neighbor distances, and that neighboring nests were more likely to share the same nest fate than non-adjacent nests. Thus, at small spatial scales, density-dependence appears to operate in the opposite direction as predicted: closer nearest neighbors are more likely to be successful. We suggest that local nest dispersion, rather than larger-scale measures of nest density per se, may play a more important role in density-dependent nest predation.

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“…For instance, in Soay sheep ( Ovis aries ) at Hirta (St Kilda Archipelago, Scotland), broad climatic indices are robust predictors (better than local weather) of population change because pulses of mortality (mainly by starvation resulting from crowding) consistently occur from January to May every year [59]. The relative effects of climate and density feedback have also been quantified in groups of sympatric species, e.g., large ungulates [60], [61], ducks [62], diurnal or nocturnal butterflies [9], [11], and flatfish [63]. Moreover, latitude has often been used as a proxy for climate, with numerous studies reporting the predominance of immediate versus delayed density feedback (and contrasting dynamics from damping through cycles or chaos) along large latitudinal bands, especially in small rodents, pest insects and game homeotherms (reviewed in [64]).…”

Section: Discussionmentioning

confidence: 99%

Spatial Climate Patterns Explain Negligible Variation in Strength of Compensatory Density Feedbacks in Birds and Mammals

et al. 2014

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The use of long-term population data to separate the demographic role of climate from density-modified demographic processes has become a major topic of ecological investigation over the last two decades. Although the ecological and evolutionary mechanisms that determine the strength of density feedbacks are now well understood, the degree to which climate gradients shape those processes across taxa and broad spatial scales remains unclear. Intuitively, harsh or highly variable environmental conditions should weaken compensatory density feedbacks because populations are hypothetically unable to achieve or maintain densities at which social and trophic interactions (e.g., competition, parasitism, predation, disease) might systematically reduce population growth. Here we investigate variation in the strength of compensatory density feedback, from long-term time series of abundance over 146 species of birds and mammals, in response to spatial gradients of broad-scale temperature precipitation variables covering 97 localities in 28 countries. We use information-theoretic metrics to rank phylogenetic generalized least-squares regression models that control for sample size (time-series length) and phylogenetic non-independence. Climatic factors explained < 1% of the remaining variation in density-feedback strength across species, with the highest non-control, model-averaged effect sizes related to extreme precipitation variables. We could not link our results directly to other published studies, because ecologists use contrasting responses, predictors and statistical approaches to correlate density feedback and climate – at the expense of comparability in a macroecological context. Censuses of multiple populations within a given species, and a priori knowledge of the spatial scales at which density feedbacks interact with climate, seem to be necessary to determine cross-taxa variation in this phenomenon. Despite the availability of robust modelling tools, the appropriate data have not yet been gathered for most species, meaning that we cannot yet make any robust generalisations about how demographic feedbacks interact with climate.

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Geographical gradients in the population dynamics of North American prairie ducks

Cited by 74 publications

References 59 publications

Effects of weather and climate on the dynamics of animal population time series

Effects of weather and climate on the dynamics of animal population time series

Density-dependent nest predation in waterfowl: the relative importance of nest density versus nest dispersion

Spatial Climate Patterns Explain Negligible Variation in Strength of Compensatory Density Feedbacks in Birds and Mammals

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