README.md
In epcc: Simulating Populations of Ectotherms under Global Warming

epcc

The R package epcc provides several functions that allow to simulate the effects of thermal sensitivity and the exposition to different environmental temperature trends on the abundance dynamics of ectothermic populations. More specifically, parameters associated with the optimum population performance (ro) and critical thermal limits for survival (CTmin and CTmax) can be specified. For instance, it is possible to simulate if the thermal optimum is below or above the current temperature and also to determine the potential outcome when considering that the population is constituted by a thermal specialist or generalist organisms (i.e., wider or narrower thermal limit ranges). Regarding environmental temperature, the package is intended to encompass a variety of situations, ranging from predicted scenarios proposed by the Intergovernmental Group of Experts on Climate Change (IPCC) at global and local specific areas, potential trends ranging from heating or cooling pulses, and trends with different temperature variability levels through time. These potential scenarios allow simulating dissimilar trends that may occur at different latitudes and time lags. In addition, possible intraspecific non-thermal effects on population dynamics (Svanback & Bolnick, 2007; Rich et al., 2009) can also be incorporated by a specific parameter (i.e., lambda, the marginal loss to competition). The package also provides functions to assess the outcome of two common interspecific interactions, i.e., competence and predation, when the population growth of one of the species is affected by temperature (the prey population in the case of predation). In addition, a function showing an ectotherm population with age structure is presented. These functions have been developed considering global warming trends as proposed by the IPCC (2014). The package epcc has been built upon a classical ordinary differential equation (ODE) solver, i.e., the R package deSolve (Soetaert et al., 2010), but our approach involves incorporating temperature effects through time, which leads to a non-autonomous system ODE approach. For each temperature trend, epcc provides a specific function that allows visualizing variations in abundance, the corresponding carrying capacity, and temperature trends.

You can install the released version of epcc from GitHub with:

library(devtools)
install\_github(''Victor-Saldana/epcc'')

Please, if you have any questions or problems with the implementation of the package,  send an 
email to saldananunezvictor@gmail.com.

Víctor Saldaña-Núñez, Fernando Córdova-Lepe, Felipe N. Moreno-Gómez.

IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth 
Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. 
Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp.

Rich,, H. B., Quinn, T. P., Scheuerell, M. D., & Schindler, D. E. (2009). Climate and intraspecific 
competition control the growth and life history of juvenile sockeye salmon (Oncorhynchus nerka) in Iliamna 
Lake, Alaska. Canadian Journal of Fisheries and Aquatic Sciences, 66(2), 238-246.doi:10.1139/f08-210

Soetaert, K., Petzoldt, T., & Setzer, R. (2010). Solving Differential Equations in R: Package deSolve. 
Journal of Statistical Software, 33(9), 1 - 25. doi:http://dx.doi.org/10.18637/jss.v033.i09

Svanback, R., & Bolnick, D. I. (2007). Intraspecific competition drives increased resource use diversity within 
a natural population. Proceedings of the Royal Society B: Biological Sciences, 274(1611), 839-844. 
doi:10.1098/rspb.2006.0198