title: "fitBirdBiomassModel" author: "Tati Micheletti" date: "10 October 2020" output: html_document:

df_print: paged

Overview

This module creates a model using total biomass of soft and hardwood for a boreal songbird (i.e. Canada Warbler). The covariates can be used in an anticipation function for harvesting.

Usage

```{r module_usage}

library("SpaDES") usrEmail = if (pemisc::user() %in% c("Tati", "tmichele")) "tati.micheletti@gmail.com" else NULL googledrive::drive_auth(email = usrEmail) setPaths(modulePath = dirname(getwd()), cachePath = checkPath(file.path(getwd(), "cache"), create = TRUE), outputPath = checkPath(file.path(getwd(), "outputs"), create = TRUE), inputPath = checkPath(file.path(getwd(), "data"), create = TRUE)) getPaths() # shows where the 4 relevant paths are

times <- list(start = 1, end = 1)

parameters <- list()

To determine the tree species to use

modules <- list("fitBirdBiomassModel") objects <- list() inputs <- list() outputs <- list()

mySim <- simInit(times = times, params = parameters, modules = modules, objects = objects)

mySimOut <- spades(mySim) ```

Events

prepData 1. Get knn data for biomass, crop it to the study area 2. Get LCC05 data -- we assume all classifications match 2001 3. Get the bird data from eBird's file 4. Extract from LCC05 and knn the species' biomasses per species and the LCC cover type 5. Sum the biomass of each type of tree: softwood or hardwood 6. Make up the table with: observationCounts, biomass softwood, biomass hardwood createModels 7. Make the glm models: observationCounts ~ softwood + hardwood biomass createCovarTables 8. Get the covariates and put it on a table

Data dependencies

Input data

By default, all the data needed is automatically downloaded from GDrive. The data comes from eBirds. The default model fits RBNU data in BC for the year 2005. If you want to provide your own data, you can provide the following objects: dataURL, dataFile and dataArchive. See module's metadata for further information. The syntax is:

{r url, echo = TRUE, eval = FALSE} objects <- list( "dataURL" = "https://drive.google.com/open?id=1hUvj5PHNDWe1VWZReciBUt4wQHAF3Gw5", "dataFile" = "ebd_CA-BC_rebnut_relAug-2019.txt", "dataArchive" = "ebd_CA-BC_rebnut_relAug-2019.zip" )

You can also provide which tree species to use with similar syntax:

{r treeSp, echo = TRUE, eval = FALSE} objects <- list( "treeSp" = c("Pice_Eng", "Pice_Gla", "Pice_Mar", "Pinu_Con", "Popu_Tre", "Pseu_Men") )

The model can handle both each individual species' biomass, or concatenate soft and hardwood ones. As default, it will use all species provided as covariates in the model. To concatenate and model exclusively soft and hardwood, set the parameter woodType = TRUE as:

{r wood, echo = TRUE, eval = FALSE} parameters <- list( "fitBirdBiomassModel" = list( "woodType" = TRUE ) )

And pass the tree species that compose soft and the ones that compose hardwood. There is a default for all treeSp (as described above), softwood and hardwood as:

{r softhard, echo = TRUE, eval = FALSE} softwoodSpecies <- c("Pice_Eng", "Pice_Gla", "Pice_Mar", "Pinu_Con", "Pseu_Men") hardwoodSpecies <- c("Popu_Tre")

However, if you provide a mismatching treeSp and softwoodSpecies or hardwoodSpecies list, it will return an error.

You can also pass another 'studyArea' but if you want to use another species, it is necessary to pass the url, targetFile and archive where the data is in GoogleDrive or another address. Also, the data NEEDS to have the collowing fields, identically written (eBird compatible): 'OBSERVATION COUNT', 'LATITUDE', 'LONGITUDE', 'OBSERVATION DATE'. While LATITUDE and LONGITUDE need to be in latlong format, OBSERVATION DATE needs to be in the format: YYYY-MM-DD.

At last, you can provide different months of data collection to compose the model (i.e. differences among seasons). Default is to summer months 5:8.

Output data

The main output is covarTable, which contains the covariates of the glm model. It can be accessed: ```{r covarTable, echo = TRUE, eval = FALSE}

mySimOut$covarTable

(Intercept) PiceEng PiceGla PiceMar PinuCon PopuTre PseuMen -0.755884580 0.009350159 0.015259595 -0.062907135 -0.009657841 0.006337240 0.040552720 ```