ibartomeus

is.invasive()

##Description##

#This function check which species (both plants and animals) are considered "invaders" somewhere in the
# world.  For that end, it checks GISD (http://www.issg.org/database/welcome/) and returns a value, either 
#"Not invasive" or the brief description presented in GISD. Note that the webpage contains more 
#information. Also note that the function won't tell you if it's exotic in your area, a lot of exotic 
#species are not considered invaders (yet). As expected, the function is as good as the database is, which 
#I find quite reliable and well maintained. The database is also able to recognize a lot (but not all) of 

ibartomeus

#who are the pollinators? (a plot)
#http://ibartomeus.wordpress.com/2012/12/17/who-are-the-pollinators

#enter data manually
d <- matrix(nrow = 6, ncol = 7)
d[,1] <- c(20.000,  10,	10,	10,	200.000,	100)
d[,2] <- c(6.000,	4,	8,	9,	100.000,	20)
d[,3] <- c(14.500,	6,	4,	9,	80.000,	4)
d[,4] <- c(30.000,	7,	2,	7,	50.000,	3)
d[,5] <- c(0.328, 10,	2,	3,	1.000,	5)

ibartomeus

library(plotrix)
#manually enter data
d <- data.frame(
  Bee_group = c("Bombus", "Dark", "Green", "Lasioglossum"),
  Efficiency = c(106.86207, 81.50000, 80.66667, 59.27586),
  Eff_SE = c(15.56551, 20.12130, 17.97529, 12.25689),
  Visitation = c(2.8333333, 0.2222222, 2.2222222, 5.6666667),
  Visit_SE = c(0.8333333, 0.1008317, 0.7119684, 1.0966378)
  )
#create a grid with value x (visitation), y (efficiency) and z = x*y.

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# This approach to assess multifunctionality is based in the idea that sites providing
    # best multiple functions will have not only a high mean value across function 
    # (approach 3 in Byrnes et al.) but also low variability in the function delivered  
    # across functions (i.e. Coef of var).

#I use Byrnes multifunc package to ilustrate it.
library(devtools)
install_github("multifunc", "jebyrnes")
library(multifunc)
library(ggplot2)

ibartomeus

---
title: "Preferring a preference index"
author: "I. Bartomeus"
output: html_document
---

I've been reading about preference indexes lately, speciphically for characterizing pollinator preferences for plants, so here is what I learnt. Preference is defined as using an item (e.g. plant) more than expected given the item abundance. 

First I like to use a quantitative framework (you can use ranks-based indices as in Williams et al 2011, which has nice propiertiest too). The simpliest quantitative index is the forage ratio:

ibartomeus

#This shows how to get the random slopes and CI's for each level in a hierarchical model

#dataset used
head(iris)

#what we want to investigate
#Is there a general relationship? and how it differs by species
plot(iris$Sepal.Width ~ iris$Petal.Width, col = iris$Species, las =1)

#Our model with random slope and intercept

ibartomeus

#Explore CIS data


#load data----
load("barometro_enero.RData")
head(barometro)
str(barometro)
head(nombres_etiquetas)
nombres_etiquetas

ibartomeus

#SevillaR talk

#The problem:

time <- c(2000:2015)
abundance <- rnorm(16, 150, 50) #poison??
plot(abundance ~ time, t = "l")


#can I detect a trend?

ibartomeus

---
title: "Sevillarusers - ggplot2 intro"
author: "Ra?l Ortiz"
date: "Tuesday, October 27, 2015"
output: pdf_document
---

# Introducci?n al paquete gr?fico "ggplot2".

## Establezco el directorio de trabajo.

ibartomeus

#I have >1000 bees to check its name, so I want to automatize taxize for
# fixing misspellings when possible
# updating synonims to accepted names
# keeping ONLY accepted species (fully resolved at species level)

# this uses taxize > 0.7.6.9157 If you are using older version (e.g. what its now on CRAN) see the history of this file.
library(taxize)
library(dplyr)

#example: good, synomin, typo, unexisting, genus only.