Thermal physiology and urbanization: perspectives on exit, entry and transformation rules
Steven L. Chown and Grant A. Duffy
Supplementary information
Methods for figure 4
MODIS climate data
Monthly composites of daytime land surface temperature data (LST; MOD11C3) were retrieved from the NASA Land Processes Distributed Active Archive Center
(LP DAAC 2015). These remote-sensed data contained monthly composites of daytime LST at a 0.05° global resolution from March 2000 to February 2015. A global climate layer of the maximum monthly temperature for this period was generated. This climate layer was used for all subsequent analyses of environmental temperature and represents the warmest monthly LST that each cell of the map has experienced in the twenty-first century.
Figure 4a: Warming tolerance data
Thermal tolerance data for a range of insect taxa were taken from Hoffman et al.
(2013) with additional insect thermal tolerance data added from Sunday et al.
(2014).
Duplicate records were deleted and where data differed between the two datasets (e.g. due to rounding), the data from Hoffman et al.
(2013) was given priority. Using the spatial coordinates of each thermal tolerance sampling point, maximum environmental temperature was extracted for each point from the MODIS climate
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layer. Warming tolerance was calculated by subtracting the maximum environmental temperature from the upper thermal limit (critical themal maximum, upper lethal temperature where 50% of the population died (ULT50), or upper lethal temperature where 100% of the population died (ULT100)) of each point.
Figure 4a: Urban-adjusted warming tolerance
The nearest city to each insect thermal tolerance sampling point was calculated based on great-circle distance between the sampling point and a list of cities with ≥ 1 million inhabitants (www.citypopulation.de). Distances between sampling points and cities varied. High latitude points in the Southern Hemisphere were furthest from the nearest city (4108.0 km), though these represent an extreme example and most points were less than 1000 km from the nearest city (Q1 = 39.6, median = 113.3, mean =
532.4, Q3 = 1100.0 km). The maximum temperature from the MODIS climate layer within each of these cities was used to calculate an urban-adjusted warming tolerance value for each point that displaced the insect to the nearest city.
Figure 4b: Maximum urban heat island estimates
Estimates of maximum urban head island (UHI) were generated for all cities with ≥ 1 million inhabitants (www.citypopulation.de). A 1°x1° grid was drawn around each city’s central coordinate and all maximum temperature values within this grid were extracted from the MODIS maximum monthly temperature layer. The mean value of all cells identified as non-urban (Schneider et al.
2009, 2010) was calculated to represent the non-urban background maximum temperature. The difference between this background maximum temperature and the highest maximum temperature value
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of cells identified as urban (Schneider et al. 2009, 2010) was used to estimate the maximum UHI temperature increase of each city.
All analyses were performed in R Statistical Software (R Core Team 2015) using the
‘raster’ (Hijmans & van Etten 2015) and ‘ggplot2’ (Wickham 2009) packages.
References
Hoffmann, A.A., Chown, S.L. & Clusella-Trullas, S. (2013) Upper thermal limits in terrestrial ectotherms: how constrained are they? Functional Ecology , 27 , 934-949.
Hijmans, R.J. & van Etten, J. (2015). raster: Geographic analysis and modeling with raster data . http://CRAN.R-project.org/package=raster
LP DAAC (2015). NASA Land Processes Distributed Active Archive Center.
MODIS/Terra 11C3. USGS/Earth Resources Observation and Science (EROS)
Center, Sioux Falls, South Dakota.
R Core Team (2015). R: A language and environment for statistical computing . R
Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.
Schneider, A., Friedl, M.A. & Potere, D. (2009) A new map of global urban extent from MODIS data. Environmental Research Letters , 4 , article 044003.
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Schneider, A., Friedl, M.A. & Potere, D. (2010) Mapping global urban areas using
MODIS 500-m data: New methods and datasets based on “urban ecoregions.” Remote
Sensing of Environment , 114 , 1733–1746.
Sunday, J.M., Bates, A.E., Kearney, M.R., Colwell, R.K., Dulvy, N.K., Longino, J.T.
& Huey, R.B. (2014) Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proceedings of the National Academy of
Sciences of the U.S.A.
, 111 , 5610–5615.
Wickham, H. (2009). ggplot2: Elegant graphics for data analysis . Springer New
York.
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