Get environmental temperatures
AmP estimation  


Contents
Background and Motivation
All rates and ages depend on temperature. Temperature is stored as auxilliary data and is usually specified in the mydata file, and used for calculating model predictions.
Correcting for temperature using the Arrhenius temperature correction model amounts to a simple transformation of ages and rates by multiplying or dividing by temperature correction factor (c_T). What is important to keep in mind though is that metabolic rates depend on temperature in a non linear way. If the temperature was fluctuating rather than constant, the mean temperature has a nonstraightforward relationship with the mean c_T.
A number of data sets (field or laboratory) are acquired at nonconstant (fluctuating) temperatures. One is tempted to calculate the mean value of temperatures (T_mean) and then correct the metabolic rates with the c_T for that (T_mean) temperature. The problem arises, however, when the experienced (fluctuating) temperatures had a larger effect in low or high part of the range, which is often the case.
The presented procedure shows how to: 1. Obtain environmental temperature values if temperatures are not reported but the geographic location is known; 2. Use the temperature values in combination with the Arrhenius temperature correction model, and 3. Obtain and use c_T as a function of time. For a concrete example including code for R see below.
The final result is either a (single) temperature (for zerovariate data) or a vector of temperatures (for Univariate data) that are taking into account speciesspecific metabolic traits, and are thus more appropriate in the context of energetic modeling.
Method
Environmental temperature as a function of time
If you do not have data about environmental temperature associated with your dataset, you can use one of these options.
 Option 1: Use R package link RNCEP: global weather and climate data at your fingertips to make a query and download environmental temperature data for a given geographic location and a specific timeframe.
 Option 2: Use R to query a global microclimate dataset. These estimates are for the middle day of each month, based on longterm average macroclimates.
 Option 3: If working with a terrestrial ectotherm, you can improve accuracy of your temperature data (obtained via any of the previous steps) by simulating behavior of the species of interest with the NicheMapR microclimate model.
Defining parameters of the Arrhenius temperature correction model
 Option 1: Use data on (developmental, metabolic, or other) rates at several different temperatures to estimate parameters of the Arrhenius temperature correction model (T_ref, T_A, T_AL, T_AH, T_L, T_H).
 Option 2: Find the Arrhenius parameters for your species in literature, and input them directly into your script.
c_T as a function of time and using the backcalculated temperature
Using the temperature correction model, calculate the temperature correction (c_T) for each temperature point obtained in Step 1.
For associating a temperature with a zerovariate data (e.g. age at puberty at a given food level) calculate the mean of all c_T values. Then convert the obtained mean(c_T) to the equivalent temperature using again the Arrhenius parameters. The backcalculated temperature is the 'constant temperature equivalent'.
For obtaining temperatures for specific time points in Univariate data, you will need to interpolate using the pairs of timec_T values you have obtained in previous steps. It is possible to save either the c_T values or back calculated temperature values as a .csv file. This can then be called in the mydata file and stored in the auxData structure.
Example
Here you can find an example where we use the sand lizard, Lacerta agilis, which is in the AmP collection and has field growth observations for a number of locations in Russia. The document describes stepbystep the procedure (including NicheMapR)and provides R code you can use and modify.