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Stemkovski, M.

Publications and source records attributed to Stemkovski, M..

3 recordsLinked to original sources

Disorder or a new order: how climate change affects phenological variability

Advancing spring phenology is a well-documented consequence of anthropogenic climate change, but it is not well understood how climate change will affect the variability of phenology year-to-year. Species phenological timings reflect adaptation to a broad suite of abiotic needs (e.g. thermal energy) and biotic interactions (e.g. predation and pollination), and changes in patterns of variability may disrupt those adaptations and interactions. Here, we present a geographically and taxonomically broad analysis of phenological shifts, temperature sensitivity, and changes in inter-annual variance encompassing nearly 10,000 long-term phenology time-series representing over 1,000 species across much of the northern hemisphere. We show that early-season species in colder and less seasonal regions were the most sensitive to temperature change and had the least variable phenologies. The timings of leaf-out, flowering, insect first-occurrence, and bird arrival have all shifted earlier and tend to be less variable in warmer years. This has led leaf-out and flower phenology to become moderately but significantly less variable over time. These simultaneous changes in phenological averages and the variation around them have the potential to influence mismatches among interacting species that are difficult to anticipate if shifts in average are studied in isolation.

ecology↗

AREAdata: a worldwide climate dataset averaged across spatial units at different scales through time

In an era of increasingly cross-discipline collaborative science, it is imperative to produce data resources which can be quickly and easily utilised by non-specialists. In particular, climate data often require heavy processing before they can be used for analyses. Here we describe AREAdata, a free-to-use online global climate dataset, pre-processed to provide the averages of various climate variables across differing administrative units (e.g., countries, states). These are daily estimates, based on the Copernicus Climate Data Stores ERA-5 data, regularly updated to the near-present and provided as direct downloads from our website (https://pearselab.github.io/areadata/). The daily climate estimates from AREAdata are consistent with other openly available data, but at much finer-grained spatial and temporal scales than available elsewhere. AREAdata complements the existing suite of climate resources by providing these data in a form more readily usable by researchers unfamiliar with GIS data-processing methods, and we anticipate these resources being of particular use to environmental and epidemiological researchers.

ecology↗

The rate of ecological acclimation is the dominant uncertainty in long-term projections of a key ecosystem service

Rapid climate change may exceed ecosystems capacity to respond through processes including phenotypic plasticity, compositional turnover and evolutionary adaption. However, research predicting impacts of climate change on ecosystem services rarely consider this rate of "ecosystem acclimation." Combining statistical models fit to historical climate data and remotely-sensed estimates of herbaceous productivity with an ensemble of climate models, we demonstrate that assumptions concerning acclimation rates are a dominant source of uncertainty: models assuming minimal acclimation project widespread decreases in forage production in the western US by 2100, while models assuming that acclimation keeps pace with climate change project widespread forage increases. Uncertainty related to ecosystem acclimation is larger than uncertainties from variation among climate models or emissions pathways. A better understanding of ecosystem acclimation is essential to improve long-term forecasts of ecosystem services, and shows that management to facilitate ecosystem acclimation may be necessary to maintain ecosystem services at historical baselines.

ecology↗