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Observations of changes in phenology have provided some of the strongest signals of the effects of climate change on terrestrial ecosystems. The International Tundra Experiment (ITEX), initiated in the early 1990s, established a common protocol to measure plant phenology in tundra study areas across the globe. Today, this valuable collection of phenology measurements depicts the responses of plants at the colder extremes of our planet to experimental and ambient changes in temperature over the past decades. The database contains 150,434 phenology observations of 278 plant species taken at 28 study areas for periods of 1 to 26 years. Here we describe the full dataset to increase the visibility and use of these data in global analyses, and to invite phenology data contributions from underrepresented tundra locations. Portions of this tundra phenology database have been used in three recent syntheses, some datasets are expanded, others are from entirely new study areas, and the entirety of these data are now available at the Polar Data Catalogue (https://doi.org/10.21963/13215). 

Snow is an important driver of ecosystem processes in cold biomes. Snow accumulation determines ground temperature, light conditions, and moisture availability during winter. It also affects the growing season’s start and end, and plant access to moisture and nutrients. Here, we review the current knowledge of the snow cover’s role for vegetation, plant-animal interactions, permafrost conditions, microbial processes, and biogeochemical cycling. We also compare studies of natural snow gradients with snow experimental manipulation studies to assess time scale difference of these approaches. The number of tundra snow studies has increased considerably in recent years, yet we still lack a comprehensive overview of how altered snow conditions will affect these ecosystems. Specifically, we found a mismatch in the timing of snowmelt when comparing studies of natural snow gradients with snow manipulations. We found that snowmelt timing achieved by snow addition and snow removal manipulations (average 7.9 days advance and 5.5 days delay, respectively) were substantially lower than the temporal variation over natural spatial gradients within a given year (mean range 56 days) or among years (mean range 32 days). Differences between snow study approaches need to be accounted for when projecting snow dynamics and their impact on ecosystems in future climates. 

Abstract Shifts in species geographic distributions in response to climate change have spurred numerous studies to determine which abiotic (e.g. climatic) and, less commonly, biotic (e.g. competitive) processes determine range limits. However, the impact of disturbances on range limits and their interactions with climatic and biotic effects is not well understood, despite their potential to alter competitive relationships between species or override climatic effects. Disturbance might have differential effects at contrasting range limits, based on Darwin's theory that biotic interactions set abiotically benign range limits and abiotic factors set abiotically stressful range limits.We predicted that plants at lower elevation (abiotically benign) range limits experience a net positive effect of disturbance, whereas those at higher elevation (abiotically stressful) range limits experience a net neutral effect. We examined plant populations along elevational gradients in the Colorado Rocky Mountains, in order to quantify the effects of human trampling disturbance at lower and upper elevational range limits of the common alpine cushion plantsSilene acaulisandMinuartia obtusiloba.Our results are consistent with Darwin's theory. A disturbance‐mediated reduction of competitive effects increases the performance of cushion plants at lower elevations, suggesting a range limit set by biotic factors. At higher elevations, where biotic interactions are minimal, disturbance has neutral or negative effects on cushion plants.Synthesis and applications. Human trampling disturbance exerts differential effects on alpine cushion plant populations at contrasting range limits, emphasizing the need to account for the effects of climate change into the management and conservation of disturbed areas. Disturbance can diminish plant–plant competitive interactions at lower elevational range limits, and thus possibly stabilize alpine species populations susceptible to climate change‐mediated encroachment by lower elevation species. Conservation and management approaches should therefore particularly account for the differential effects of disturbance across climatic gradients. 

ABSTRACT MotivationHere, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables IncludedThe database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and GrainSampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and GrainThe earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of MeasurementThe database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Formatcsv and. SQL.