Search for: All records

Abstract AimClimate change is broadly affecting phenology, but species‐specific phenological response to temperature is not well understood. In streams, insect emergence has important ecosystem‐level consequences because emergent adults link aquatic and terrestrial food webs. We quantified emergence timing and duration (within‐population synchronicity) of insects among streams along a spatiotemporal gradient of mean water temperature in a montane basin to assess the sensitivity of these phenological traits to heat accumulation from mid‐winter through spring emergence periods. LocationSix headwater streams in the Lookout Creek basin, H.J. Andrews Experimental Forest, Oregon, USA. MethodsWe collected emerging adults of four abundant insect species twice weekly throughout spring for 6 consecutive years. We fit Gaussian models to the empirical temporal distributions to characterize peak emergence timing (mean) and duration (days between 5th and 95th percentiles) for each species/stream/year combination. We then quantified relationships between degree‐day accumulation and phenological response. ResultsOnly one of the four species (a caddisfly) showed a simple response of earlier emergence timing in both warmer streams and years. One stonefly had lengthy emergence periods resulting in substantial phenological overlap between warmer and cooler streams/years. Interestingly, two species (a mayfly and a stonefly) responded strongly to temporal (interannual) temperature differences but minimally to spatial differences, indicating that emergence was nearly synchronous among streams, within years. These two species had among‐stream differences approaching 500 degree‐days from mid‐winter to peak emergence. Conversely, duration of emergence was more strongly associated with spatial than temporal differences, with longer duration in lower‐elevation (warmer) streams. Main conclusionsEmergence phenology has species‐specific responses to temperature likely driven by complex cues for diapause or quiescence periods during preceding life cycle stages. We hypothesize a trade‐off between complex phenological response that synchronizes emergence among heterogeneous sites and other traits such as adult longevity and dispersal capacity. 

Abstract Our understanding of ecological processes is built on patterns inferred from data. Applying modern analytical tools such as machine learning to increasingly high dimensional data offers the potential to expand our perspectives on these processes, shedding new light on complex ecological phenomena such as pathogen transmission in wild populations. Here, we propose a novel approach that combines data mining with theoretical models of disease dynamics. Using rodents as an example, we incorporate statistical differences in the life history features of zoonotic reservoir hosts into pathogen transmission models, enabling us to bound the range of dynamical phenomena associated with hosts, based on their traits. We then test for associations between equilibrium prevalence, a key epidemiological metric and data on human outbreaks of rodent‐borne zoonoses, identifying matches between empirical evidence and theoretical predictions of transmission dynamics. We show how this framework can be generalized to other systems through a rubric of disease models and parameters that can be derived from empirical data. By linking life history components directly to their effects on disease dynamics, our mining‐modelling approach integrates machine learning and theoretical models to explore mechanisms in the macroecology of pathogen transmission and their consequences for spillover infection to humans. 

Abstract Ongoing climate change is shifting species distributions and increasing extinction risks globally. It is generally thought that large population sizes and short generation times of marine phytoplankton may allow them to adapt rapidly to global change, including warming, thus limiting losses of biodiversity and ecosystem function. Here, we show that a marine diatom survives high, previously lethal, temperatures after adapting to above‐optimal temperatures under nitrogen (N)‐replete conditions. N limitation, however, precludes thermal adaptation, leaving the diatom vulnerable to high temperatures. A trade‐off between high‐temperature tolerance and increased N requirements may explain why N limitation inhibited adaptation. Because oceanic N limitation is common and likely to intensify in the future, the assumption that phytoplankton will readily adapt to rising temperatures may need to be reevaluated.