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The existence of patterns in population dynamics across species geographic ranges and climatic niches is a pervasive idea in ecology. Population variability (i.e. temporal variability in population density) should hypothetically increase near range edges or niche limits because of less suitable environments in these areas, but the occurrence of such patterns remains largely unexplored. Further, fluctuations in temperature could pose demographic constraints on populations and also influence their variability. We used Breeding Bird Survey data to show that the population variability of 97 resident North American birds consistently increases towards their niche limits and in areas with more variable temperatures, but not towards their geographic range edges. However, our model has limited explanatory power, and phylogenetic history and species traits could not explain these results. These findings suggest that other factors, such as biotic interactions and resource availability, might be more important drivers of population variability in resident North American birds. 

Populations fluctuate over time and across geographical space, and understanding how different factors contribute to population variability is a central goal in population ecology. There is a particular interest in identifying trends of population variability within geographical ranges as population densities of species can fluctuate substantially across geographical space. A common assumption is that populations vary more near species geographical range edges because of unsuitable environments and higher vulnerability to environmental variability in these areas. However, empirical data rarely support this expectation, suggesting that population variability is not related to its position within species geographical ranges. We propose that performance curves, which describe the relationship between population growth rates and environmental conditions, can be used to disentangle geographical patterns of population variability. Performance curves are important for understanding population variability because populations fluctuate more in locations where they have lower growth rates owing to unsuitable environmental conditions. This is important for the assessment of these geographical patterns in population variability because geographical edges often do not reflect environmental edges. Considering species performance curves when evaluating geographical patterns of population variability would also allow researchers to detect populations that are more susceptible to future changes in environmental conditions. 

Abstract The availability of suitable niche space constrains where species can occur geographically. This tie between niche space and geographic space is crucial when estimating species geographic distributions in a changing climate. However, specific combinations of climatic conditions may be overrepresented in geographic space, highlighting the potential disconnect between climatic niche area and geographic range size.We develop a niche density estimator that accounts for the geographic availability of climatic niche space, relate this to traditional estimates of niche area and explore how these niche estimates are related to species geographic range size.To do this, we use data on over 230,000 species recorded in the Global Biodiversity Information Facility, providing a thorough test of the sensitivity of niche estimation technique on geographic range size–climatic niche scaling relationships, and clarifying the link between geographic space and environmental space by considering the density of available environments in environmental space.Niche density was more strongly related to species geographic range size than niche area, highlighting the role of the geographic availability of climatic niche space in biogeographic relationships. As species geographic ranges and environmental conditions change, understanding the ecological and evolutionary determinants of this positive scaling between geographic range size and niche size is an important research frontier. 

Abstract AimAbundance–occupancy relationships posit that more locally abundant species occupy more sites than less abundant species. Although widely supported, the occurrence and detection of abundance–occupancy relationships is sensitive to sampling and detection processes. Data from large‐scale standardized sampling efforts are key to address abundance–occupancy relationships. We aimed to use such a dataset to evaluate the occurrence of abundance–occupancy relationships across different spatial grains and over time for aquatic and terrestrial taxa. LocationUSA. Time period2014–2019. Major taxa studiedBirds, mammals, beetles, ticks, fishes, macroinvertebrates and zooplankton. MethodsSpecies abundance and occupancy data were obtained from the National Ecological Observatory Network (NEON). Species mean abundance and occupancy (fraction of sampled locations that were occupied) were estimated for three different spatial grains (i.e., plot, site and domain) for all years sampled. Linear models were used to explore the consistency of interspecific abundance–occupancy relationships. The slope coefficients of these models were related to temporal and spatial variables and to species richness while controlling for taxa in a linear mixed‐effects model (LMM) framework. ResultsWe found evidence for positive abundance–occupancy relationships across the three spatial grains and over time for all taxa we studied. However, our linear models had low explanatory power, suggesting that relationships, although general, were weak. Abundance–occupancy relationships were slightly stronger at the smallest spatial grain than at the largest spatial grain, but showed no detectable change over time for any taxa. Finally, species richness was not associated with the strength of these relationships. Main conclusionsTogether, our results suggest that positive interspecific abundance–occupancy relationships are fairly general but are not capable of explaining substantial variation in spatial patterns of abundance, and that other factors, such as species traits and niche, are also likely to influence these relationships.