adjustment_trend: Adjustment to observed population trends

In Victor-Saldana/epcc: Simulating Populations of Ectotherms under Global Warming

Adjustment to observed population trends

Description

This function allows to fit the observed time series of abundance and ambient temperature trends together with the main descriptors of the TPCs for the salamander Desmognathus ochrophaeus.

Usage

adjustment_trend(
  y_ini = c(N = 15),
  temp_cmin = 4,
  temp_cmax = 33.1,
  roi = 0.3,
  lambdai = 0.05,
  trend = 1
)

Arguments

y_ini	Initial population for adjustment.
temp_cmin	Minimum critical temperature for Desmognathus ochrophaeus (Layne & Claussen, 1987).
temp_cmax	Maximum critical temperature for Desmognathus ochrophaeus (Markle & Kozak 2018).
roi	Initial value for optimal intrinsic growth rate.
lambdai	Initial value for the marginal loss of competition.
trend	Acronym corresponding to the type of temperature trend used.

Details

This function allows to fit the abundance trends reported for the salamander Desmognathus ochrophaeus in the BioTIME database (Wiley, 2016; Dornelas et al., 2018), the temperature data obtained from the NOAA platform (www.ncdc.noaa.gov), the descriptors of TPCs reported by Layne & Claussen (1987) and by Markle & Kozak (2018), using the nls2 function we can find the parameters ro and lambda, which are necessary to fit the abundance curve to the proposed model. Caution has to be taken to the sensitivity to the initial parameters to make adjustments, therefore the following temperature trends are presented in an initial proposal, the enumeration of which represents the option to be given in the "trend" argument of the function: 1: Projection of increasing linear temperatura, 2: Projection of decreasing linear temperatura 3: Periodic increasing temperature trend, 4: Periodic decreasing temperature trend, 5: Constant temperature projection.

References

Dornelas M, Antão LH, Moyes F, Bates, AE, Magurran, AE, et al.(2018). BioTIME: A database of biodiversity time series for the Anthropocene. Global Ecol Biogeogr; 27:760 - 786. https://doi.org/10.1111/geb.12729.

Grothendieck, G. (2013). nls2: Non-Linear Regression with Brute Force. R package version 0.2, URL http://CRAN.R-project.org/package=nls2.

Layne, J. R., & Claussen, D. L. (1987). Time courses of thermal acclimation for critical thermal minima in the salamanders Desmognathus quadramaculatus, Desmognathus monticola, Desmognathus ochrophaeus, and Plethodon jordani. Comparative Biochemistry and Physiology Part A: Physiology, 87(4), 895–898. doi:10.1016/0300-9629(87)90011-9

Markle, T. M., & Kozak, K. H. (2018). Low acclimation capacity of narrow-ranging thermal specialists exposes susceptibility to global climate change. Ecology and Evolution, 8(9), 4644–4656.doi:10.1002/ece3.4006.

NOAA National Centers for Environmental Information, State of the Climate: Global Climate Report for Annual 2020, published online January 2021, retrieved on September 4, 2021 from https://www.ncdc.noaa.gov/sotc/global/202013.

Wiley, R. H. (2016). “Population estimates of Appalachian salamanders”. Coweeta LTER. Available at: http://coweeta.uga.edu/eml/1044.xml.

Examples

## Not run: 
######################################################################
  #Example 1:Fitting observed time series of abundance and ambient
 #           temperature, using an increasing linear temperature trend.
#######################################################################

adjustment_trend(y_ini = c(N = 15),
                temp_cmin = 4,
                temp_cmax = 33.1,
                roi = 0.3,
                lambdai = 0.05,
                trend = 1)

######################################################################
  #Example 2:Fitting the observed time series of abundance and ambient
         #   temperature, using an periodic increasing temperature trend.
#######################################################################

adjustment_trend(y_ini = c(N = 15),
                temp_cmin = 4,
                temp_cmax = 33.1,
                roi = 0.3,
                lambdai = 0.05,
                rend = 3)

######################################################################
  #Example 3:Fitting the observed time series of abundance and ambient
           # temperature, using a constant temperature trend.
#######################################################################

adjustment_trend(y_ini = c(N = 15),
                temp_cmin = 4,
                temp_cmax = 33.1,
                roi = 0.3,
                lambdai = 0.05,
                trend = 5)

## End(Not run)

Victor-Saldana/epcc documentation built on Oct. 18, 2023, 6:57 a.m.