Spatial climate patterns explain negligible variation in strength of compensatory density feedbacks in birds and mammals

Surviving drought: a framework for understanding animal responses to small rain events in the arid zone

Large rain events drive dramatic resource pulses and the complex pulse-reserve dynamics of arid ecosystems change between high-rain years and drought. However, arid-zone animal responses to short-term changes in climate are unknown, particularly smaller rain events that briefly interrupt longer-term drought. Using arthropods as model animals, we determined the effects of a small rain event on arthropod abundance in western New South Wales, Australia during a longer-term shift toward drought. Arthropod abundance decreased over 2 yr, but captures of 10 out of 15 ordinal taxa increased dramatically after the small rain event (<40 mm). The magnitude of increases ranged from 10.4 million% (collembolans) to 81% (spiders). After 3 months, most taxa returned to prerain abundance. However, small soil-dwelling beetles, mites, spiders, and collembolans retained high abundances despite the onset of winter temperatures and lack of subsequent rain. As predicted by pulse-reserve models, most arid-zone arthropod populations declined during drought. However, small rain events may play a role in buffering some taxa from declines during longer-term drought or other xenobiotic influences. We outline the framework for a new model of animal responses to environmental conditions in the arid zone, as some species clearly benefit from rain inputs that do not dramatically influence primary productivity.

Detecting population-environmental interactions with mismatched time series data

Time series analysis is an essential method for decomposing the influences of density and exogenous factors such as weather and climate on population regulation. However, there has been little work focused on understanding how well commonly collected data can reconstruct the effects of environmental factors on population dynamics. We show that, analogous to similar scale issues in spatial data analysis, coarsely sampled temporal data can fail to detect covariate effects when interactions occur on timescales that are fast relative to the survey period. We propose a method for modeling mismatched time series data that couples high-resolution environmental data to low-resolution abundance data. We illustrate our approach with simulations and by applying it to Florida's southern Snail kite population. Our simulation results show that our method can reliably detect linear environmental effects and that detecting nonlinear effects requires high-resolution covariate data even when the population turnover rate is slow. In the Snail kite analysis, our approach performed among the best in a suite of previously used environmental covariates explaining Snail kite dynamics and was able to detect a potential phenological shift in the environmental dependence of Snail kites. Our work provides a statistical framework for reliably detecting population-environment interactions from coarsely surveyed time series. An important implication of this work is that the low predictability of animal population growth by weather variables found in previous studies may be due, in part, to how these data are utilized as covariates.

Environmental variability and population dynamics: do European and North American ducks play by the same rules?

Density dependence, population regulation, and variability in population size are fundamental population processes, the manifestation and interrelationships of which are affected by environmental variability. However, there are surprisingly few empirical studies that distinguish the effect of environmental variability from the effects of population processes. We took advantage of a unique system, in which populations of the same duck species or close ecological counterparts live in highly variable (north American prairies) and in stable (north European lakes) environments, to distinguish the relative contributions of environmental variability (measured as between-year fluctuations in wetland numbers) and intraspecific interactions (density dependence) in driving population dynamics. We tested whether populations living in stable environments (in northern Europe) were more strongly governed by density dependence than populations living in variable environments (in North America). We also addressed whether relative population dynamical responses to environmental variability versus density corresponded to differences in life history strategies between dabbling (relatively "fast species" and governed by environmental variability) and diving (relatively "slow species" and governed by density) ducks. As expected, the variance component of population fluctuations caused by changes in breeding environments was greater in North America than in Europe. Contrary to expectations, however, populations in more stable environments were not less variable nor clearly more strongly density dependent than populations in highly variable environments. Also, contrary to expectations, populations of diving ducks were neither more stable nor stronger density dependent than populations of dabbling ducks, and the effect of environmental variability on population dynamics was greater in diving than in dabbling ducks. In general, irrespective of continent and species life history, environmental variability contributed more to variation in species abundances than did density. Our findings underscore the need for more studies on populations of the same species in different environments to verify the generality of current explanations about population dynamics and its association with species life history.