Land use causes genetic differentiation of life-history traits in Bromus hordeaceus

There is increasing evidence that species can evolve rapidly in response to environmental change. However, although land use is one of the key drivers of current environmental change, studies of its evolutionary consequences are still fairly scarce, in particular studies that examine land-use effects across large numbers of populations, and discriminate between different aspects of land use. Here, we investigated genetic differentiation in relation to land use in the annual grass Bromus hordeaceus. A common garden study with offspring from 51 populations from three regions and a broad range of land-use types and intensities showed that there was indeed systematic population differentiation of ecologically important plant traits in relation to land use, in particular due to increasing mowing and grazing intensities. We also found strong land-use-related genetic differentiation in plant phenology, where the onset of flowering consistently shifted away from the typical time of management. In addition, increased grazing intensity significantly increased the genetic variability within populations. Our study suggests that land use can cause considerable genetic differentiation among plant populations, and that the timing of land use may select for phenological escape strategies, particularly in monocarpic plant species.

Phenology for tropoclimatological surveys and large-scale mapping

Biotic and abiotic phenological observations can be collected from continental to local spatial scale. Plant phenological observations may only be recorded wherever there is vegetation. Fog, snow and ice are available as phenological para-meters wherever they appear. The singularity of phenological observations is the possibility of spatial intensification to a microclimatic scale where the equipment of meteorological measurements is too expensive for intensive campaigning. The omnipresence of region-specific phenological parameters allows monitoring for a spatially much more detailed assessment of climate change than with weather data. We demonstrate this concept with phenological observations with the use of a special network in the Canton of Berne, Switzerland, with up to 600 observations sites (more than 1 to 10 km² of the inhabited area). Classic cartography, gridding, the integration into a Geographic Information System GIS and large-scale analysis are the steps to a detailed knowledge of topoclimatic conditions of a mountainous area. Examples of urban phenology provide other types of spatially detailed applications. Large potential in phenological mapping in future analyses lies in combining traditionally observed species-specific phenology with remotely sensed and modelled phenology that provide strong spatial information. This is a long history from cartographic intuition to algorithm-based representations of phenology.

Origins of the word "phenology"

Observing and documenting life cycle stages of plants and animals have been tradition and necessity for humans throughout history. Phenological observations—as called by their modern scientific name—were key to successful hunting and farming because the precise knowledge of animal behavior and plant growth, as well as their timing with changing seasons, was critical for survival. In today's context of environmental awareness and climate change research, phenological observations have become prime indicators of documenting altered life cycles due to environmental change in disciplines from biology to climatology, geography, and environmental history. Observations on the ground, from space, and from models of different complexity describe intra-annual and interannual changes of life cycles at individual, pixel, or grid box scale.

A 170 year spring phenology index of plants in eastern China

Extending phenological records into the past is essential for the understanding of past ecological change and evaluating the effects of climate change on ecosystems. A growing body of historical phenological information is now available for Europe, North America, and Asia. In East Asia, long-term phenological series are still relatively scarce. This study extracted plant phenological observations from old diaries in the period 1834–1962. A spring phenology index (SPI) for the modern period (1963–2009) was defined as the mean flowering time of three shrubs (first flowering of Amygdalus davidiana and Cercis chinensis, 50% of full flowering of Paeonia suffruticosa) according to the data availability. Applying calibrated transfer functions from the modern period to the historical data, we reconstructed a continuous SPI time series across eastern China from 1834 to 2009. In the recent 30 years, the SPI is 2.1–6.3 days earlier than during any other consecutive 30 year period before 1970. A moving linear trend analysis shows that the advancing trend of SPI over the past three decades reaches upward of 4.1 d/decade, which exceeds all previously observed trends in the past 30 year period. In addition, the SPI series correlates significantly with spring (February to April) temperatures in the study area, with an increase in spring temperature of 1°C inducing an earlier SPI by 3.1 days. These shifts of SPI provide important information regarding regional vegetation-climate relationships, and they are helpful to assess long term of climate change impacts on biophysical systems and biodiversity.

Parameterization of temperature sensitivity of spring phenology and its application in explaining diverse phenological responses to temperature change

Existing evidence of plant phenological change to temperature increase demonstrates that the phenological responsiveness is greater at warmer locations and in early-season plant species. Explanations of these findings are scarce and not settled. Some studies suggest considering phenology as one functional trait within a plant's life history strategy. In this study, we adapt an existing phenological model to derive a generalized sensitivity in space (SpaceSens) model for calculating temperature sensitivity of spring plant phenophases across species and locations. The SpaceSens model have three parameters, including the temperature at the onset date of phenophases (Tp), base temperature threshold (Tb) and the length of period (L) used to calculate the mean temperature when performing regression analysis between phenology and temperature. A case study on first leaf date of 20 plant species from eastern China shows that the change of Tp and Tb among different species accounts for interspecific difference in temperature sensitivity. Moreover, lower Tp at lower latitude is the main reason why spring phenological responsiveness is greater there. These results suggest that spring phenophases of more responsive, early-season plants (especially in low latitude) will probably continue to diverge from the other late-season plants with temperatures warming in the future.