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Climate-Driven Phenological Change: Developing Robust Spatiotemporal Modeling and Projection Capability.

Prieto C, Destouni G - PLoS ONE (2015)

Bottom Line: Our possibility to appropriately detect, interpret and respond to climate-driven phenological changes depends on our ability to model and predict the changes.A modeling methodology capable of handling such complexities can be a powerful tool for phenological change projection.Model application to observed warming over the past 60 years demonstrates the model usefulness for assessment of climate-driven first flight change.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Geography, Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden.

ABSTRACT
Our possibility to appropriately detect, interpret and respond to climate-driven phenological changes depends on our ability to model and predict the changes. This ability may be hampered by non-linearity in climate-phenological relations, and by spatiotemporal variability and scale mismatches of climate and phenological data. A modeling methodology capable of handling such complexities can be a powerful tool for phenological change projection. Here we develop such a methodology using citizen scientists' observations of first flight dates for orange tip butterflies (Anthocharis cardamines) in three areas extending along a steep climate gradient. The developed methodology links point data of first flight observations to calculated cumulative degree-days until first flight based on gridded temperature data. Using this methodology we identify and quantify a first flight model that is consistent across different regions, data support scales and assumptions of subgrid variability and observation bias. Model application to observed warming over the past 60 years demonstrates the model usefulness for assessment of climate-driven first flight change. The cross-regional consistency of the model implies predictive capability for future changes, and calls for further application and testing of analogous modeling approaches to other species, phenological variables and parts of the world.

No MeSH data available.


Related in: MedlinePlus

Investigated regions, temperature grid cells and sighting data points.a) Location and extent of the three study regions in Sweden. b-d) Spatial distribution of first flight (FF) sighting points (circles) within each temperature grid cell (gray squares) for the regions: b) Medelpad/Ångermanland, c) Sörmland/Stockholm and d) Skåne. All sighting data (all circles) for all years with such data are considered as FF data under the spatial variability assumption (SVA). Only the white-circled data points, with each representing an earliest sighting for at least one year with data, are considered as FF data under the observation bias assumption (OBA).
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pone.0141207.g001: Investigated regions, temperature grid cells and sighting data points.a) Location and extent of the three study regions in Sweden. b-d) Spatial distribution of first flight (FF) sighting points (circles) within each temperature grid cell (gray squares) for the regions: b) Medelpad/Ångermanland, c) Sörmland/Stockholm and d) Skåne. All sighting data (all circles) for all years with such data are considered as FF data under the spatial variability assumption (SVA). Only the white-circled data points, with each representing an earliest sighting for at least one year with data, are considered as FF data under the observation bias assumption (OBA).

Mentions: For the three study regions in Sweden (Fig 1A), we use reported field observations of orange tip butterflies (Anthocharis cardamines; Fig 1B–1D). Observation data are available continuously for the years 2003–2010 in the citizen scientist database Artportalen [8], along with additional data sporadically available for some earlier years. This database provides date and species observed at different spatially referenced locations within the study areas. Temperature data is considered from the E-OBS European dataset [9] for the period 1950–2010 in terms of daily maximum (Tmax) and minimum (Tmin) gridded data values. In particular, we use version 5.0 of the blended data interpolated on a regular grid with cell sizes of 0.25°long × 0.25°lat (gray squares in Fig 1B–1D). According to Haylock et al. [9], this interpolation was carried out by a three step methodology including an initial homogenization of the observed daily station data and the use of a kriging method that was selected to be the best for interpolation of daily anomalies.


Climate-Driven Phenological Change: Developing Robust Spatiotemporal Modeling and Projection Capability.

Prieto C, Destouni G - PLoS ONE (2015)

Investigated regions, temperature grid cells and sighting data points.a) Location and extent of the three study regions in Sweden. b-d) Spatial distribution of first flight (FF) sighting points (circles) within each temperature grid cell (gray squares) for the regions: b) Medelpad/Ångermanland, c) Sörmland/Stockholm and d) Skåne. All sighting data (all circles) for all years with such data are considered as FF data under the spatial variability assumption (SVA). Only the white-circled data points, with each representing an earliest sighting for at least one year with data, are considered as FF data under the observation bias assumption (OBA).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4636262&req=5

pone.0141207.g001: Investigated regions, temperature grid cells and sighting data points.a) Location and extent of the three study regions in Sweden. b-d) Spatial distribution of first flight (FF) sighting points (circles) within each temperature grid cell (gray squares) for the regions: b) Medelpad/Ångermanland, c) Sörmland/Stockholm and d) Skåne. All sighting data (all circles) for all years with such data are considered as FF data under the spatial variability assumption (SVA). Only the white-circled data points, with each representing an earliest sighting for at least one year with data, are considered as FF data under the observation bias assumption (OBA).
Mentions: For the three study regions in Sweden (Fig 1A), we use reported field observations of orange tip butterflies (Anthocharis cardamines; Fig 1B–1D). Observation data are available continuously for the years 2003–2010 in the citizen scientist database Artportalen [8], along with additional data sporadically available for some earlier years. This database provides date and species observed at different spatially referenced locations within the study areas. Temperature data is considered from the E-OBS European dataset [9] for the period 1950–2010 in terms of daily maximum (Tmax) and minimum (Tmin) gridded data values. In particular, we use version 5.0 of the blended data interpolated on a regular grid with cell sizes of 0.25°long × 0.25°lat (gray squares in Fig 1B–1D). According to Haylock et al. [9], this interpolation was carried out by a three step methodology including an initial homogenization of the observed daily station data and the use of a kriging method that was selected to be the best for interpolation of daily anomalies.

Bottom Line: Our possibility to appropriately detect, interpret and respond to climate-driven phenological changes depends on our ability to model and predict the changes.A modeling methodology capable of handling such complexities can be a powerful tool for phenological change projection.Model application to observed warming over the past 60 years demonstrates the model usefulness for assessment of climate-driven first flight change.

View Article: PubMed Central - PubMed

Affiliation: Department of Physical Geography, Bolin Centre for Climate Research, Stockholm University, Stockholm, Sweden.

ABSTRACT
Our possibility to appropriately detect, interpret and respond to climate-driven phenological changes depends on our ability to model and predict the changes. This ability may be hampered by non-linearity in climate-phenological relations, and by spatiotemporal variability and scale mismatches of climate and phenological data. A modeling methodology capable of handling such complexities can be a powerful tool for phenological change projection. Here we develop such a methodology using citizen scientists' observations of first flight dates for orange tip butterflies (Anthocharis cardamines) in three areas extending along a steep climate gradient. The developed methodology links point data of first flight observations to calculated cumulative degree-days until first flight based on gridded temperature data. Using this methodology we identify and quantify a first flight model that is consistent across different regions, data support scales and assumptions of subgrid variability and observation bias. Model application to observed warming over the past 60 years demonstrates the model usefulness for assessment of climate-driven first flight change. The cross-regional consistency of the model implies predictive capability for future changes, and calls for further application and testing of analogous modeling approaches to other species, phenological variables and parts of the world.

No MeSH data available.


Related in: MedlinePlus