From cb1bb59eba1aaebfaadd2b1dcc43c348eb6e73c3 Mon Sep 17 00:00:00 2001 From: Matt Strimas-Mackey Date: Thu, 20 Jul 2023 14:25:47 -0700 Subject: [PATCH] workshop 1 edits --- docs/ebird.html | 570 ++++++++++-------- .../ebird-applications-mapping-map-1.png | Bin 225042 -> 216978 bytes docs/index.html | 16 +- docs/search.json | 12 +- docs/sitemap.xml | 6 +- ebird.qmd | 65 +- index.qmd | 12 +- 7 files changed, 367 insertions(+), 314 deletions(-) diff --git a/docs/ebird.html b/docs/ebird.html index 4628aa0..5c1893f 100644 --- a/docs/ebird.html +++ b/docs/ebird.html @@ -231,50 +231,49 @@

1.3 Importing eBird data into R

The previous step left us with two tab separated text files, one for the EBD (i.e. observation data) and one for the SED (i.e. checklist data). Start a new RStudio project and put the downloaded text files in the data/ sub-directory of the project directory.

-

The auk R package is specifically designed for working with eBird data. It contains the functions read_ebd() and read_sampling(), designed to import the EBD and SED, respectively, into R. First let’s import the checklist data (SED).

+

The auk R package is specifically designed for working with eBird data. It includes the functions read_ebd() and read_sampling() for importing the EBD and SED, respectively, into R. First let’s import the checklist data (SED).

library(auk)
 library(dplyr)
 library(ggplot2)
 library(lubridate)
 library(sf)
-library(tmap)
-
-f_sed <- "data/ebd_BR-RS_fotfly_smp_relJun-2023_sampling.txt"
-checklists <- read_sampling(f_sed, unique = FALSE)
-glimpse(checklists)
-#> Rows: 34,005
-#> Columns: 30
-#> $ last_edited_date          <chr> "2021-02-15 15:28:32.796728", "2021-12-12 14…
-#> $ country                   <chr> "Brazil", "Brazil", "Brazil", "Brazil", "Bra…
-#> $ country_code              <chr> "BR", "BR", "BR", "BR", "BR", "BR", "BR", "B…
-#> $ state                     <chr> "Rio Grande do Sul", "Rio Grande do Sul", "R…
-#> $ state_code                <chr> "BR-RS", "BR-RS", "BR-RS", "BR-RS", "BR-RS",…
-#> $ county                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ county_code               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ iba_code                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ bcr_code                  <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ usfws_code                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ atlas_block               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ locality                  <chr> "UFRGS--Estação Experimental Agronômica", "C…
-#> $ locality_id               <chr> "L10057337", "L10637103", "L10637103", "L108…
-#> $ locality_type             <chr> "H", "P", "P", "P", "H", "H", "H", "P", "P",…
-#> $ latitude                  <dbl> -30.1, -28.2, -28.2, -30.4, -32.6, -31.2, -3…
-#> $ longitude                 <dbl> -51.7, -51.7, -51.7, -51.5, -52.8, -50.8, -5…
-#> $ observation_date          <date> 1993-03-22, 1998-12-11, 1999-12-27, 1990-01…
-#> $ time_observations_started <chr> "08:00:00", "09:00:00", "09:00:00", NA, NA, …
-#> $ observer_id               <chr> "obs203567", "obs1166183", "obs1166183", "ob…
-#> $ sampling_event_identifier <chr> "S81646316", "S98801647", "S98802795", "S758…
-#> $ protocol_type             <chr> "Traveling", "Traveling", "Traveling", "Inci…
-#> $ protocol_code             <chr> "P22", "P22", "P22", "P20", "P20", "P62", "P…
-#> $ project_code              <chr> "EBIRD", "EBIRD", "EBIRD", "EBIRD", "EBIRD",…
-#> $ duration_minutes          <int> 195, 120, 120, NA, NA, 90, NA, 80, NA, NA, N…
-#> $ effort_distance_km        <dbl> 3.22, 2.00, 2.00, NA, NA, NA, NA, 4.00, NA, …
-#> $ effort_area_ha            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ number_observers          <int> 2, 1, 1, NA, NA, 2, NA, 1, 1, 2, 1, 1, 1, 2,…
-#> $ all_species_reported      <lgl> FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALS…
-#> $ group_identifier          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ trip_comments             <chr> NA, "lista feita por LB.", "lista feita por …
+ +f_sed <- "data/ebd_BR-RS_fotfly_smp_relJun-2023_sampling.txt" +checklists <- read_sampling(f_sed, unique = FALSE) +glimpse(checklists) +#> Rows: 34,005 +#> Columns: 30 +#> $ last_edited_date <chr> "2021-02-15 15:28:32.796728", "2021-12-12 14… +#> $ country <chr> "Brazil", "Brazil", "Brazil", "Brazil", "Bra… +#> $ country_code <chr> "BR", "BR", "BR", "BR", "BR", "BR", "BR", "B… +#> $ state <chr> "Rio Grande do Sul", "Rio Grande do Sul", "R… +#> $ state_code <chr> "BR-RS", "BR-RS", "BR-RS", "BR-RS", "BR-RS",… +#> $ county <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, … +#> $ county_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, … +#> $ iba_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, … +#> $ bcr_code <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, … +#> $ usfws_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, … +#> $ atlas_block <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, … +#> $ locality <chr> "UFRGS--Estação Experimental Agronômica", "C… +#> $ locality_id <chr> "L10057337", "L10637103", "L10637103", "L108… +#> $ locality_type <chr> "H", "P", "P", "P", "H", "H", "H", "P", "P",… +#> $ latitude <dbl> -30.1, -28.2, -28.2, -30.4, -32.6, -31.2, -3… +#> $ longitude <dbl> -51.7, -51.7, -51.7, -51.5, -52.8, -50.8, -5… +#> $ observation_date <date> 1993-03-22, 1998-12-11, 1999-12-27, 1990-01… +#> $ time_observations_started <chr> "08:00:00", "09:00:00", "09:00:00", NA, NA, … +#> $ observer_id <chr> "obs203567", "obs1166183", "obs1166183", "ob… +#> $ sampling_event_identifier <chr> "S81646316", "S98801647", "S98802795", "S758… +#> $ protocol_type <chr> "Traveling", "Traveling", "Traveling", "Inci… +#> $ protocol_code <chr> "P22", "P22", "P22", "P20", "P20", "P62", "P… +#> $ project_code <chr> "EBIRD", "EBIRD", "EBIRD", "EBIRD", "EBIRD",… +#> $ duration_minutes <int> 195, 120, 120, NA, NA, 90, NA, 80, NA, NA, N… +#> $ effort_distance_km <dbl> 3.22, 2.00, 2.00, NA, NA, NA, NA, 4.00, NA, … +#> $ effort_area_ha <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, … +#> $ number_observers <int> 2, 1, 1, NA, NA, 2, NA, 1, 1, 2, 1, 1, 1, 2,… +#> $ all_species_reported <lgl> FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALS… +#> $ group_identifier <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, … +#> $ trip_comments <chr> NA, "lista feita por LB.", "lista feita por …
@@ -289,7 +288,7 @@

Take some time to explore the variables in the checklist dataset. If you’re unsure about any of the variables, consult the metadata document that came with the data download (eBird_Basic_Dataset_Metadata_v1.14.pdf).

-

Next we’ll import the observation data.

+

Next, let’s import the observation data.

f_ebd <- "data/ebd_BR-RS_fotfly_smp_relJun-2023.txt"
 observations <- read_ebd(f_ebd, unique = FALSE, rollup = FALSE)
@@ -359,7 +358,7 @@ 
Take some time to explore the variables in the observation dataset. Notice that the EBD duplicates many of the checklist-level variables from the SED.
-

When we read the data into R you probably noticed we used unique = FALSE and rollup = FALSE. By default the read functions in auk perform two important pre-processing steps: combining duplicate shared checklists and taxonomic rollup. We intentionally turned off this functionality for the purposes of demonstration.

+

When we read the data into R, we used unique = FALSE and rollup = FALSE. By default the read functions in auk perform two important pre-processing steps: combining duplicate shared checklists and taxonomic rollup. We intentionally turned off this functionality for the purposes of demonstration.

1.3.1 Shared checklists

eBird allows users to share checklists with other eBirders in their group, for example this checklist is shared by 10 observers. These checklists can be identified by looking at the group_identifier variable, which assigns an ID connecting all checklists in the group.

@@ -394,10 +393,32 @@

tail(checklists_unique$checklist_id) #> [1] "G7592371" "G7592372" "G7573316" "G7594361" "G7633009" "G7603956" +
+
+
+ +
+
+Tip +
+
+
+

Curious when checklists and observers contributed to a shared checklist after it has been collapsed? The sampling_event_identifier and observer_id contain comma-separated lists of all checklists and observers that went into the shared checklists.

+
+
checklists_unique %>% 
+  filter(checklist_id == "G10019405") %>% 
+  select(checklist_id, group_identifier, sampling_event_identifier, observer_id)
+#> # A tibble: 1 × 4
+#>   checklist_id group_identifier sampling_event_identifier observer_id         
+#>   <chr>        <chr>            <chr>                     <chr>               
+#> 1 G10019405    G10019405        S133745869,S134107963     obs1481597,obs567598
+
+
+

1.3.2 Taxonomic rollup

-

eBird observations can be made at levels below species (e.g. subspecies) or above species (e.g. a bird that was identified as a duck, but the species could not be determined); however, for most uses we’ll want observations at the species level. This is especially true if we want to produce detection/non-detection data from complete checklists because “complete” only applies to species.

+

eBird observations can be made at levels below species (e.g. subspecies) or above species (e.g. a bird that was identified as a duck, but the species could not be determined); however, for most uses we’ll want observations at the species level. This is especially true if we want to produce detection/non-detection data from complete checklists because “complete” only applies at the species level.

@@ -413,148 +434,163 @@

auk_rollup() drops all observations not identifiable to a species and rolls up all observations reported below species to the species level.

-
observations_rollup <- auk_rollup(observations)
-# only one checklist is affected by this
-observations %>% 
-  filter(sampling_event_identifier == "S99335111") %>% 
-  select(sampling_event_identifier, common_name, subspecies_common_name)
-#> # A tibble: 2 × 3
-#>   sampling_event_identifier common_name            subspecies_common_name       
-#>   <chr>                     <chr>                  <chr>                        
-#> 1 S99335111                 Fork-tailed Flycatcher <NA>                         
-#> 2 S99335111                 Fork-tailed Flycatcher Fork-tailed Flycatcher (sava…
-observations_rollup %>% 
-  filter(sampling_event_identifier == "S99335111") %>% 
-  select(sampling_event_identifier, common_name)
-#> # A tibble: 1 × 2
-#>   sampling_event_identifier common_name           
-#>   <chr>                     <chr>                 
-#> 1 S99335111                 Fork-tailed Flycatcher
+
observations_rollup <- auk_rollup(observations)
+# only one checklist is affected by this
+observations %>% 
+  filter(sampling_event_identifier == "S99335111") %>% 
+  select(sampling_event_identifier, common_name, subspecies_common_name, 
+         observation_count)
+#> # A tibble: 2 × 4
+#>   sampling_event_identifier common_name subspecies_common_name observation_count
+#>   <chr>                     <chr>       <chr>                  <chr>            
+#> 1 S99335111                 Fork-taile… <NA>                   X                
+#> 2 S99335111                 Fork-taile… Fork-tailed Flycatche… 1
+observations_rollup %>% 
+  filter(sampling_event_identifier == "S99335111") %>% 
+  select(sampling_event_identifier, common_name,
+         observation_count)
+#> # A tibble: 1 × 3
+#>   sampling_event_identifier common_name            observation_count
+#>   <chr>                     <chr>                  <chr>            
+#> 1 S99335111                 Fork-tailed Flycatcher X
+
+
+
+
+ +
+
+Tip +
+
+
+

If multiple taxa on a single checklist roll up to the same species, auk_rollup() attempts to combine them intelligently. If each observation has a count, those counts are added together, but if any of the observations is missing a count (i.e. the count is “X”) the combined observation is also assigned an “X”.

+

1.4 Generating detection/non-detection data

-

Complete eBird checklists are extremely valuable because, for all species that weren’t reported, we can infer counts of 0. This allows us to convert eBird from presence only data to detection/non-detection data, which allows for much more robust analyses. Note that we don’t use the term presence/absence data here because a non-detection doesn’t necessarily imply the species was absent, only that observer didn’t detect and identify it.

+

Complete eBird checklists are extremely valuable because, for all species that weren’t reported, we can infer counts of 0. This allows us to convert eBird from presence only data to detection/non-detection data, which allows for much more robust analyses. Note that we don’t use the term presence/absence data here because a non-detection doesn’t necessarily imply the species was absent, only that the observer didn’t detect and identify it.

We refer to the process of producing detection/non-detection data as “zero-filling” the eBird data because we’re filling in the missing zeros. We’ll read the eBird data into R again, filter to only complete checklists, then use the function auk_zerofill() to generate detection/non-detection data. Note that shared checklists are combined and taxonomic rollup is performed by default when using the read_*() functions from auk.

-
# import checklist data
-checklists <- read_sampling(f_sed) %>% 
-  # subset to complete checklists
-  filter(all_species_reported)
-# import observation data
-observations <- read_ebd(f_ebd) %>% 
-  # subset to complete checklists
-  filter(all_species_reported)
-# zero-fill to produce detection/non-detection data
-zf <- auk_zerofill(observations, checklists, collapse = TRUE)
-glimpse(zf)
-#> Rows: 22,838
-#> Columns: 38
-#> $ checklist_id              <chr> "S98801647", "S98802795", "S91164181", "S114…
-#> $ last_edited_date          <chr> "2021-12-12 14:52:43.115062", "2022-10-25 03…
-#> $ country                   <chr> "Brazil", "Brazil", "Brazil", "Brazil", "Bra…
-#> $ country_code              <chr> "BR", "BR", "BR", "BR", "BR", "BR", "BR", "B…
-#> $ state                     <chr> "Rio Grande do Sul", "Rio Grande do Sul", "R…
-#> $ state_code                <chr> "BR-RS", "BR-RS", "BR-RS", "BR-RS", "BR-RS",…
-#> $ county                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ county_code               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ iba_code                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ bcr_code                  <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ usfws_code                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ atlas_block               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ locality                  <chr> "Capela Sagrado Coração de Jesus,RS,BR", "Ca…
-#> $ locality_id               <chr> "L10637103", "L10637103", "L15616802", "L165…
-#> $ locality_type             <chr> "P", "P", "P", "P", "H", "P", "P", "P", "P",…
-#> $ latitude                  <dbl> -28.2, -28.2, -29.3, -29.3, -29.5, -29.3, -3…
-#> $ longitude                 <dbl> -51.7, -51.7, -49.7, -49.7, -50.6, -50.3, -5…
-#> $ observation_date          <date> 1998-12-11, 1999-12-27, 1989-12-27, 1992-04…
-#> $ time_observations_started <chr> "09:00:00", "09:00:00", "07:30:00", NA, NA, …
-#> $ observer_id               <chr> "obs1166183", "obs1166183", "obs501289", "ob…
-#> $ sampling_event_identifier <chr> "S98801647", "S98802795", "S91164181", "S114…
-#> $ protocol_type             <chr> "Traveling", "Traveling", "Traveling", "Hist…
-#> $ protocol_code             <chr> "P22", "P22", "P22", "P62", "P62", "P22", "P…
-#> $ project_code              <chr> "EBIRD", "EBIRD", "EBIRD", "EBIRD", "EBIRD",…
-#> $ duration_minutes          <int> 120, 120, 80, NA, NA, 180, NA, NA, 120, 480,…
-#> $ effort_distance_km        <dbl> 2.000, 2.000, 4.000, NA, NA, 5.000, NA, NA, …
-#> $ effort_area_ha            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ number_observers          <int> 1, 1, 1, 1, 2, 1, 3, 3, 12, 12, 12, 2, 1, NA…
-#> $ all_species_reported      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…
-#> $ group_identifier          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ trip_comments             <chr> "lista feita por LB.", "lista feita por LB."…
-#> $ scientific_name           <chr> "Tyrannus savana", "Tyrannus savana", "Tyran…
-#> $ breeding_code             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ breeding_category         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ behavior_code             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ age_sex                   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
-#> $ observation_count         <chr> "X", "X", "0", "0", "0", "0", "0", "0", "4",…
-#> $ species_observed          <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALS…
+
# import checklist data
+checklists <- read_sampling(f_sed) %>% 
+  # subset to complete checklists
+  filter(all_species_reported)
+# import observation data
+observations <- read_ebd(f_ebd) %>% 
+  # subset to complete checklists
+  filter(all_species_reported)
+# zero-fill to produce detection/non-detection data
+zf <- auk_zerofill(observations, checklists, collapse = TRUE)
+glimpse(zf)
+#> Rows: 22,838
+#> Columns: 38
+#> $ checklist_id              <chr> "S98801647", "S98802795", "S91164181", "S114…
+#> $ last_edited_date          <chr> "2021-12-12 14:52:43.115062", "2022-10-25 03…
+#> $ country                   <chr> "Brazil", "Brazil", "Brazil", "Brazil", "Bra…
+#> $ country_code              <chr> "BR", "BR", "BR", "BR", "BR", "BR", "BR", "B…
+#> $ state                     <chr> "Rio Grande do Sul", "Rio Grande do Sul", "R…
+#> $ state_code                <chr> "BR-RS", "BR-RS", "BR-RS", "BR-RS", "BR-RS",…
+#> $ county                    <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ county_code               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ iba_code                  <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ bcr_code                  <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ usfws_code                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ atlas_block               <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ locality                  <chr> "Capela Sagrado Coração de Jesus,RS,BR", "Ca…
+#> $ locality_id               <chr> "L10637103", "L10637103", "L15616802", "L165…
+#> $ locality_type             <chr> "P", "P", "P", "P", "H", "P", "P", "P", "P",…
+#> $ latitude                  <dbl> -28.2, -28.2, -29.3, -29.3, -29.5, -29.3, -3…
+#> $ longitude                 <dbl> -51.7, -51.7, -49.7, -49.7, -50.6, -50.3, -5…
+#> $ observation_date          <date> 1998-12-11, 1999-12-27, 1989-12-27, 1992-04…
+#> $ time_observations_started <chr> "09:00:00", "09:00:00", "07:30:00", NA, NA, …
+#> $ observer_id               <chr> "obs1166183", "obs1166183", "obs501289", "ob…
+#> $ sampling_event_identifier <chr> "S98801647", "S98802795", "S91164181", "S114…
+#> $ protocol_type             <chr> "Traveling", "Traveling", "Traveling", "Hist…
+#> $ protocol_code             <chr> "P22", "P22", "P22", "P62", "P62", "P22", "P…
+#> $ project_code              <chr> "EBIRD", "EBIRD", "EBIRD", "EBIRD", "EBIRD",…
+#> $ duration_minutes          <int> 120, 120, 80, NA, NA, 180, NA, NA, 120, 480,…
+#> $ effort_distance_km        <dbl> 2.000, 2.000, 4.000, NA, NA, 5.000, NA, NA, …
+#> $ effort_area_ha            <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ number_observers          <int> 1, 1, 1, 1, 2, 1, 3, 3, 12, 12, 12, 2, 1, NA…
+#> $ all_species_reported      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…
+#> $ group_identifier          <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ trip_comments             <chr> "lista feita por LB.", "lista feita por LB."…
+#> $ scientific_name           <chr> "Tyrannus savana", "Tyrannus savana", "Tyran…
+#> $ breeding_code             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ breeding_category         <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ behavior_code             <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ age_sex                   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
+#> $ observation_count         <chr> "X", "X", "0", "0", "0", "0", "0", "0", "4",…
+#> $ species_observed          <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALS…

The observation_count variable has true counts as well as “X”s, which indicate that the species was detected but the number of individuals was not counted. auk_zerofill() adds a new binary column, species_observed, indicating whether or not the species was detected.

-
select(zf, observation_count, species_observed) %>% 
-  head(10)
-#> # A tibble: 10 × 2
-#>   observation_count species_observed
-#>   <chr>             <lgl>           
-#> 1 X                 TRUE            
-#> 2 X                 TRUE            
-#> 3 0                 FALSE           
-#> 4 0                 FALSE           
-#> 5 0                 FALSE           
-#> 6 0                 FALSE           
-#> # ℹ 4 more rows
+
select(zf, observation_count, species_observed) %>% 
+  head(10)
+#> # A tibble: 10 × 2
+#>   observation_count species_observed
+#>   <chr>             <lgl>           
+#> 1 X                 TRUE            
+#> 2 X                 TRUE            
+#> 3 0                 FALSE           
+#> 4 0                 FALSE           
+#> 5 0                 FALSE           
+#> 6 0                 FALSE           
+#> # ℹ 4 more rows

Let’s convert the “X”s to NAs and transform observation_count to an integer variable.

-
zf$observation_count <- if_else(zf$observation_count == "X", 
-                                NA_character_, zf$observation_count) %>% 
-  as.integer()
-select(zf, observation_count, species_observed) %>% 
-  head(10)
-#> # A tibble: 10 × 2
-#>   observation_count species_observed
-#>               <int> <lgl>           
-#> 1                NA TRUE            
-#> 2                NA TRUE            
-#> 3                 0 FALSE           
-#> 4                 0 FALSE           
-#> 5                 0 FALSE           
-#> 6                 0 FALSE           
-#> # ℹ 4 more rows
+
zf$observation_count <- if_else(zf$observation_count == "X", 
+                                NA_character_, zf$observation_count) %>% 
+  as.integer()
+select(zf, observation_count, species_observed) %>% 
+  head(10)
+#> # A tibble: 10 × 2
+#>   observation_count species_observed
+#>               <int> <lgl>           
+#> 1                NA TRUE            
+#> 2                NA TRUE            
+#> 3                 0 FALSE           
+#> 4                 0 FALSE           
+#> 5                 0 FALSE           
+#> 6                 0 FALSE           
+#> # ℹ 4 more rows

1.5 Filtering data

-

Now that you have a detection/non-detection dataset, it’s likely that you want to do something with it. For example, you may want to make a map, use the eBird data to identify priority areas for a species, or train a species distribution model. Regardless of the specific application, it’s likely that some amount of filtering of the data is required first. Some of the ways you may want to filter eBird data include:

+

Now that you have a detection/non-detection dataset, it’s likely that you want to do something with it. For example, you may want to make a map, identify priority areas for a species, or train a species distribution model. Regardless of the specific application, it’s likely that some amount of filtering of the data is required first. Some of the ways you may want to filter eBird data include:

  • Temporal filtering: filter the data to a specific range of years or to a specific time of year.
  • Spatial filtering: filter the data to focus on a specific region, e.g. a protected area.
  • Increasing precision: some eBird checklists are quite long in distance or duration leading to spatial or temporal imprecision. By removing longer checklists we can increase the spatial precision of the dataset.
  • Reducing variation in effort: unlike structured scientific surveys, data can be submitted to eBird using a variety of protocols and there is significant variation in effort between checklists in the eBird dataset. Variation in protocol and effort leads to variation in detectability (more effort generally leads to higher detectability). We can choose to impose more structure on the eBird dataset by filtering to reduce variation in protocol and effort.
-

The specific filtering you apply will depend on how you intend to use the eBird data. However, for the sake of this example, let’s filter the eBird data to only traveling and stationary checklists from 2013-2022 that are less than 6 hours in duration and 10km in length.

+

The specific filtering you apply will depend on how you intend to use the eBird data. However, for the sake of this example, let’s filter the eBird data to only traveling and stationary checklists from 2013-2022 that are less than 6 hours in duration and 10 km in length.

-
zf_filtered <- zf %>% 
-  filter(year(observation_date) >= 2013, year(observation_date) <= 2022,
-         protocol_type %in% c("Traveling", "Stationary"),
-         duration_minutes < 6 * 60,
-         effort_distance_km < 10 | protocol_type == "Stationary")
-nrow(zf)
-#> [1] 22838
-nrow(zf_filtered)
-#> [1] 16526
+
zf_filtered <- zf %>% 
+  filter(year(observation_date) >= 2013, year(observation_date) <= 2022,
+         protocol_type %in% c("Traveling", "Stationary"),
+         duration_minutes < 6 * 60,
+         effort_distance_km < 10 | protocol_type == "Stationary")
+nrow(zf)
+#> [1] 22838
+nrow(zf_filtered)
+#> [1] 16526

We reduced the number of checklists by 6,312, but the checklists remaining are of higher quality.

Finally, many of the columns in this data frame are unnecessary or redundant, so we’ll select only the columns we need.

-
checklists <- zf_filtered %>% 
-  select(checklist_id, 
-         latitude, longitude,
-         observation_date, time_observations_started,
-         protocol_type,
-         duration_minutes, effort_distance_km, number_observers,
-         observation_count, species_observed)
+
checklists_zf <- zf_filtered %>% 
+  select(checklist_id, 
+         latitude, longitude,
+         observation_date, time_observations_started,
+         protocol_type,
+         duration_minutes, effort_distance_km, number_observers,
+         observation_count, species_observed)
@@ -563,27 +599,27 @@

<

1.6.1 Detection frequency

The simplest thing we can do with these eBird observations is estimate the frequency of detection of Fork-tailed Flycatcher within Rio Grande do Sul. This is the proportion of eBird checklists in the region that detected the species. species_observed is a binary TRUE/FALSE column indicating if a species was detected and R treats TRUE as 1 and FALSE as 0, so we can take the average of this column to get the detection frequency.

-
mean(checklists$species_observed)
-#> [1] 0.15
+
mean(checklists_zf$species_observed)
+#> [1] 0.15

So, Fork-tailed Flycatcher is fairly common within this region with 15% of checklists detecting the species. Detection frequency can be used to compare the prevalence of a species between regions or over time. For example, Fork-tailed Flycatcher is migratory, so let’s look at how detection frequency changes over the months of the year.

-
monthly_detection <- checklists %>% 
-  mutate(month = month(observation_date)) %>% 
-  group_by(month) %>% 
-  summarize(detection_frequency = mean(species_observed))
-
-# plot monthly detection frequency
-ggplot(monthly_detection) +
-  aes(x = month, y = detection_frequency) +
-  geom_line() +
-  geom_point() +
-  scale_x_continuous(breaks = 1:12) +
-  scale_y_continuous(labels = scales::percent) +
-  labs(x = "Month of year",
-       y = "Detection frequency",
-       title = "Monthly detection frequency for Fork-tailed Flycatcher",
-       subtitle = "Rio Grande do Sul, Brazil")
+
monthly_detection <- checklists_zf %>% 
+  mutate(month = month(observation_date)) %>% 
+  group_by(month) %>% 
+  summarize(detection_frequency = mean(species_observed))
+
+# plot monthly detection frequency
+ggplot(monthly_detection) +
+  aes(x = month, y = detection_frequency) +
+  geom_line() +
+  geom_point() +
+  scale_x_continuous(breaks = 1:12) +
+  scale_y_continuous(labels = scales::percent) +
+  labs(x = "Month of year",
+       y = "Detection frequency",
+       title = "Monthly detection frequency for Fork-tailed Flycatcher",
+       subtitle = "Rio Grande do Sul, Brazil")
@@ -607,7 +643,7 @@

- -
+

There is significant variability in checklist submissions per month, with twice as many checklists in February as October. If we were to look at the monthly number of detections from presence-only data we would expect more detections in months with more checklists even if the prevalence of Fork-tailed Flycatcher is the same.

-
monthly_checklists <- count(checklists, month = month(observation_date))
-
-# plot monthly number of checklists
-ggplot(monthly_checklists) +
-  aes(x = month, y = n) +
-  geom_line() +
-  geom_point() +
-  scale_x_continuous(breaks = 1:12) +
-  scale_y_continuous(limits = c(0, NA), labels = scales::comma) +
-  labs(x = "Month of year",
-       y = "# of eBird checklists",
-       title = "Monthly eBird checklist submissions in Rio Grande do Sul")
+
monthly_checklists <- count(checklists_zf, month = month(observation_date))
+
+# plot monthly number of checklists
+ggplot(monthly_checklists) +
+  aes(x = month, y = n) +
+  geom_line() +
+  geom_point() +
+  scale_x_continuous(breaks = 1:12) +
+  scale_y_continuous(limits = c(0, NA), labels = scales::comma) +
+  labs(x = "Month of year",
+       y = "# of eBird checklists",
+       title = "Monthly eBird checklist submissions in Rio Grande do Sul")
@@ -648,46 +684,46 @@

1.6.2 Converting to spatial format

Many applications of eBird data require converting the data into an explicitly spatial format, for example, to make a map or subset the observations to only those within a polygon for a protected area. The R package sf can be use to represent spatial points, lines, and polygons in R. We can convert the data frame of checklists to an sf object using the latitude and longitude.

-
checklists_sf <- st_as_sf(checklists, coords = c("longitude", "latitude"),
-                          # 4326 is the code for an unprojected lon/lat
-                          # coordiante reference system
-                          crs = 4326)
-print(checklists_sf)
-#> Simple feature collection with 16526 features and 9 fields
-#> Geometry type: POINT
-#> Dimension:     XY
-#> Bounding box:  xmin: -57.6 ymin: -35 xmax: -48.7 ymax: -27.1
-#> Geodetic CRS:  WGS 84
-#> # A tibble: 16,526 × 10
-#>   checklist_id observation_date time_observations_started protocol_type
-#> * <chr>        <date>           <chr>                     <chr>        
-#> 1 S117444976   2013-05-18       07:47:00                  Traveling    
-#> 2 S117215031   2013-04-14       07:53:00                  Traveling    
-#> 3 S53740464    2013-01-11       08:00:00                  Traveling    
-#> 4 S16023053    2013-12-14       17:30:00                  Stationary   
-#> 5 S15844084    2013-12-03       13:00:00                  Traveling    
-#> 6 S117214399   2013-03-16       07:36:00                  Traveling    
-#> # ℹ 16,520 more rows
-#> # ℹ 6 more variables: duration_minutes <int>, effort_distance_km <dbl>,
-#> #   number_observers <int>, observation_count <int>, species_observed <lgl>,
-#> #   geometry <POINT [°]>
+
checklists_sf <- st_as_sf(checklists_zf, coords = c("longitude", "latitude"),
+                          # 4326 is the code for an unprojected lon/lat
+                          # coordiante reference system
+                          crs = 4326)
+print(checklists_sf)
+#> Simple feature collection with 16526 features and 9 fields
+#> Geometry type: POINT
+#> Dimension:     XY
+#> Bounding box:  xmin: -57.6 ymin: -35 xmax: -48.7 ymax: -27.1
+#> Geodetic CRS:  WGS 84
+#> # A tibble: 16,526 × 10
+#>   checklist_id observation_date time_observations_started protocol_type
+#> * <chr>        <date>           <chr>                     <chr>        
+#> 1 S117444976   2013-05-18       07:47:00                  Traveling    
+#> 2 S117215031   2013-04-14       07:53:00                  Traveling    
+#> 3 S53740464    2013-01-11       08:00:00                  Traveling    
+#> 4 S16023053    2013-12-14       17:30:00                  Stationary   
+#> 5 S15844084    2013-12-03       13:00:00                  Traveling    
+#> 6 S117214399   2013-03-16       07:36:00                  Traveling    
+#> # ℹ 16,520 more rows
+#> # ℹ 6 more variables: duration_minutes <int>, effort_distance_km <dbl>,
+#> #   number_observers <int>, observation_count <int>, species_observed <lgl>,
+#> #   geometry <POINT [°]>

Imagine that we’re only interested in observations from within 50 km of Gramado, Brazil (29.375°S, 50.876°W). We can use sf to generate a circle of radium 50 km centered on Gramado, then subset observations to only those falling within the circle.

-
gramado_point <- st_sfc(st_point(c(-50.876, -29.375)), crs = 4326)
-# 50km = 50,000m
-gramado_circle <- st_buffer(gramado_point, dist = 50000)
-checklists_gramado <- checklists_sf[gramado_circle, ]
+
gramado_point <- st_sfc(st_point(c(-50.876, -29.375)), crs = 4326)
+# 50km = 50,000m
+gramado_circle <- st_buffer(gramado_point, dist = 50000)
+checklists_gramado <- checklists_sf[gramado_circle, ]

We could now, for example, calculate the detection frequency in the area around Gramado.

-
mean(checklists_gramado$species_observed)
-#> [1] 0.097
+
mean(checklists_gramado$species_observed)
+#> [1] 0.097

Finally, in many cases, it may be more convenient to work with eBird data outside of R in a GIS such as QGIS, a free and open source GIS. We can export the Fork-tailed Flycatcher observations to a GeoPackage using sf.

-
write_sf(checklists_sf, "data/fotfly-ebird_br-rs.gpkg",
-         layer = "fotfly_observations")
+
write_sf(checklists_sf, "data/fotfly-ebird_br-rs.gpkg",
+         layer = "fotfly_observations")
@@ -699,63 +735,63 @@

-

GeoPackage is a modern, open source alternative to the shapefile format for storing spatial data. GeoPackages avoid many of the problems and limitations associated with shapefiles, and they are much more efficient than shapefiles. The online ArcGIS documentation provides instructions for how to open a GeoPackage in ArcGIS.

+

The GeoPackage is a modern, open source alternative to the shapefile format for storing spatial data. GeoPackages avoid many of the problems and limitations associated with shapefiles, and they are much more efficient than shapefiles. The online ArcGIS documentation provides instructions for how to open a GeoPackage in ArcGIS.

1.6.3 Mapping

-

Now that we have the data in a spatial format, we can produce a map of the Fork-tailed Flycatcher detection/non-detection data. For complex, highly customized maps, we recommend using a GIS such as QGIS that’s specifically designed for cartography. However, it’s possible to make a quick, simple map in R using the tmap package.

+

Now that we have the data in a spatial format, we can produce a map of the Fork-tailed Flycatcher detection/non-detection data. For complex, highly customized maps, we recommend using a GIS such as QGIS that’s specifically designed for cartography. However, it is possible to make a quick, simple map in R.

We’ll start by loading polygons defining coutry and state borders, which will provide contextual information for our map. These polygons come from Natural Earth, and excellent source of global, attribution free spatial data. The R package rnaturalearth provides access to Natural Earth data within R; however, for convenience we’ve provided the necessary layers in the file data/gis-data.gpkg included in the data package for this workshop. We’ll also project everything to an equal area projection center on Rio Grande do Sul.

-
crs <- st_crs("+proj=laea +lat_0=-30 +lon_0=-50")
-
-# polygons from natural earth
-ne_land <- read_sf("data/gis-data.gpkg", "ne_land") %>% 
-  st_transform(crs = crs)
-ne_country_lines <- read_sf("data/gis-data.gpkg", "ne_country_lines") %>% 
-  st_transform(crs = crs)
-rgds_boundary <- read_sf("data/gis-data.gpkg", "ne_states") %>% 
-  filter(state_code == "BR-RS") %>% 
-  st_transform(crs = crs)
-
-# transform ebird data to equal area projection
-checklists_proj <- st_transform(checklists_sf, crs = crs)
-
-
-
par(mar = c(0.25, 0.25, 0.25, 0.25))
+
crs <- st_crs("+proj=laea +lat_0=-30 +lon_0=-50")
 
-# set up plot area
-plot(st_geometry(checklists_proj), col = NA)
-
-# contextual gis data
-plot(ne_land, col = "#dddddd", border = "#888888", lwd = 0.5, add = TRUE)
-plot(rgds_boundary, col = "#cccccc", border = NA, add = TRUE)
-#> Warning in plot.sf(rgds_boundary, col = "#cccccc", border = NA, add = TRUE):
-#> ignoring all but the first attribute
-plot(ne_country_lines, col = "#ffffff", lwd = 1.5, add = TRUE)
-
-# ebird observations
-# not detected
-plot(filter(checklists_proj, !species_observed),
-     pch = 19, cex = 0.2, col = alpha("#555555", 0.5),
-     add = TRUE)
-#> Warning in plot.sf(filter(checklists_proj, !species_observed), pch = 19, :
-#> ignoring all but the first attribute
-# detected
-plot(filter(checklists_proj, species_observed),
-     pch = 19, cex = 0.3, col = alpha("#4daf4a", 1),
-     add = TRUE)
-#> Warning in plot.sf(filter(checklists_proj, species_observed), pch = 19, :
-#> ignoring all but the first attribute
-# legend
-legend("bottomright", bty = "n",
-       col = c("#555555", "#4daf4a"),
-       legend = c("eBird checklists", "Fork-tailed Flycatcher sightings"),
-       pch = 19)
-box()
-par(new = TRUE, mar = c(0, 0, 3, 0))
-title("Fork-tailed Flycatcher eBird Observations\n2013-2022")

+# polygons from natural earth
+ne_land <- read_sf("data/gis-data.gpkg", "ne_land") %>% 
+  st_transform(crs = crs)
+ne_country_lines <- read_sf("data/gis-data.gpkg", "ne_country_lines") %>% 
+  st_transform(crs = crs)
+rgds_boundary <- read_sf("data/gis-data.gpkg", "ne_states") %>% 
+  filter(state_code == "BR-RS") %>% 
+  st_transform(crs = crs)
+
+# transform ebird data to equal area projection
+checklists_proj <- st_transform(checklists_sf, crs = crs)
+
+

Now we can make a map of observations of Fork-tailed Flycatcher in Rio Grande do Sul. We build up the map in layers, first creating a basemap with the Natural Earth polygons, then plotting the eBird detection and non-detection data on top. When building up a map in layes like this, it’s often useful to first plot a blank version the main dataset you intend to map to define the spatial extent of the map, then layer everything else on top, finishing with plotting the data a second time so it appears as the top layer.

+
+
par(mar = c(0.25, 0.25, 2, 0.25))
+
+# start with a blank plot of the data to define the spatial extent of the map
+plot(st_geometry(checklists_proj), col = NA)
+
+# contextual gis data
+plot(ne_land, col = "#dddddd", border = "#888888", lwd = 0.5, add = TRUE)
+plot(rgds_boundary, col = "#cccccc", border = NA, add = TRUE)
+#> Warning in plot.sf(rgds_boundary, col = "#cccccc", border = NA, add = TRUE):
+#> ignoring all but the first attribute
+plot(ne_country_lines, col = "#ffffff", lwd = 1.5, add = TRUE)
+
+# ebird observations
+# not detected
+plot(filter(checklists_proj, !species_observed),
+     pch = 19, cex = 0.2, col = alpha("#555555", 0.5),
+     add = TRUE)
+#> Warning in plot.sf(filter(checklists_proj, !species_observed), pch = 19, :
+#> ignoring all but the first attribute
+# detected
+plot(filter(checklists_proj, species_observed),
+     pch = 19, cex = 0.3, col = alpha("#4daf4a", 1),
+     add = TRUE)
+#> Warning in plot.sf(filter(checklists_proj, species_observed), pch = 19, :
+#> ignoring all but the first attribute
+# legend
+legend("bottomright", bty = "n",
+       col = c("#555555", "#4daf4a"),
+       legend = c("eBird checklists", "Fork-tailed Flycatcher sightings"),
+       pch = 19)
+box()
+title("Fork-tailed Flycatcher eBird Observations (2013-2022)")
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eBird Workshops OCA II

Introduction

-

The contents of this website comprise the notes for a workshop on best practices for using eBird data and eBird Status data products presented at the Ornithological Congress of The Americas (OCA) in August 2023 in Gramado, Brazil. The workshop is divided into two lessons covering:

+

This website contains the notes for a set of two workshops on best practices for using eBird data and eBird Status data products, respectively, presented at the Ornithological Congress of The Americas (OCA) in August 2023 in Gramado, Brazil. The two workshops are:

  1. Best Practices for Using eBird Data: introduction to the eBird Basic Dataset (EBD), challenges associated with using eBird data for analysis, and best practices for preparing eBird data for modeling.
  2. eBird Status and Trends: downloading eBird Status data products, loading the data into R, and using them for a variety of applications.

Setup

-

This workshop is intended to be interactive. All examples are written in the R programming language, and the instructor will work through the examples in real time, while the attendees are encouraged following along by writing the same code. To ensure we can avoid any unnecessary delays, please follow these setup instructions prior to the workshop

+

This workshop is intended to be interactive. All examples are written in the R programming language, and the instructor will work through the examples in real time, while the attendees are encouraged following along by writing the same code. To ensure we can avoid any unnecessary delays, please follow these setup instructions prior to the workshop:

    -
  1. Create an eBird account if you don’t already have one and request access to the raw eBird data and/or the eBird Status data products depending on which workshops you’re attending:
    2. -
+
  • Create an eBird account if you don’t already have one and request access to the raw eBird data and/or the eBird Status data products depending on which workshops you’re attending: -
      +
    1. Download and install the latest version of R. You must have R version 4.0.0 or newer to follow along with this workshop
    2. Download and install the latest version of RStudio. RStudio is not required for this workshop; however, the instructors will be using it and you may find it easier to following along if you’re working in the same environment.
    3. The lessons in this workshop use a variety of R packages. To install all the necessary packages, run the following code
      4. @@ -221,12 +219,12 @@

      Setup

    1. Ensure all packages are updated to their most recent versions by clicking on the Update button on the Packages tab in RStudio.
      2. -
    2. Download the data package for the workshop you are attending:
      3. -
    +
  • Download the data package for the workshop you are attending: +
    • +

    Tidyverse

    diff --git a/docs/search.json b/docs/search.json index 813d943..41e8c24 100644 --- a/docs/search.json +++ b/docs/search.json @@ -4,14 +4,14 @@ "href": "index.html", "title": "eBird Workshops OCA II", "section": "", - "text": "Introduction\nThe contents of this website comprise the notes for a workshop on best practices for using eBird data and eBird Status data products presented at the Ornithological Congress of The Americas (OCA) in August 2023 in Gramado, Brazil. The workshop is divided into two lessons covering:" + "text": "Introduction\nThis website contains the notes for a set of two workshops on best practices for using eBird data and eBird Status data products, respectively, presented at the Ornithological Congress of The Americas (OCA) in August 2023 in Gramado, Brazil. The two workshops are:" }, { "objectID": "index.html#sec-intro-setup", "href": "index.html#sec-intro-setup", "title": "eBird Workshops OCA II", "section": "Setup", - "text": "Setup\nThis workshop is intended to be interactive. All examples are written in the R programming language, and the instructor will work through the examples in real time, while the attendees are encouraged following along by writing the same code. To ensure we can avoid any unnecessary delays, please follow these setup instructions prior to the workshop\n\nCreate an eBird account if you don’t already have one and request access to the raw eBird data and/or the eBird Status data products depending on which workshops you’re attending:\n\n\nBest Practices for Using eBird Data: request access to the eBird Basic Dataset.\neBird Status and Trends: request access to the eBird Status data products\n\n\nDownload and install the latest version of R. You must have R version 4.0.0 or newer to follow along with this workshop\nDownload and install the latest version of RStudio. RStudio is not required for this workshop; however, the instructors will be using it and you may find it easier to following along if you’re working in the same environment.\nThe lessons in this workshop use a variety of R packages. To install all the necessary packages, run the following code\n\n\nif (!requireNamespace(\"remotes\", quietly = TRUE)) {\n install.packages(\"remotes\")\n}\nremotes::install_github(\"ebird/ebird-best-practices\")\n\n\nEnsure all packages are updated to their most recent versions by clicking on the Update button on the Packages tab in RStudio.\nDownload the data package for the workshop you are attending:\n\n\nBest Practices for Using eBird Data\neBird Status and Trends" + "text": "Setup\nThis workshop is intended to be interactive. All examples are written in the R programming language, and the instructor will work through the examples in real time, while the attendees are encouraged following along by writing the same code. To ensure we can avoid any unnecessary delays, please follow these setup instructions prior to the workshop:\n\nCreate an eBird account if you don’t already have one and request access to the raw eBird data and/or the eBird Status data products depending on which workshops you’re attending:\n\nBest Practices for Using eBird Data: request access to the eBird Basic Dataset.\neBird Status and Trends: request access to the eBird Status data products\n\nDownload and install the latest version of R. You must have R version 4.0.0 or newer to follow along with this workshop\nDownload and install the latest version of RStudio. RStudio is not required for this workshop; however, the instructors will be using it and you may find it easier to following along if you’re working in the same environment.\nThe lessons in this workshop use a variety of R packages. To install all the necessary packages, run the following code\n\n\nif (!requireNamespace(\"remotes\", quietly = TRUE)) {\n install.packages(\"remotes\")\n}\nremotes::install_github(\"ebird/ebird-best-practices\")\n\n\nEnsure all packages are updated to their most recent versions by clicking on the Update button on the Packages tab in RStudio.\nDownload the data package for the workshop you are attending:\n\nBest Practices for Using eBird Data\neBird Status and Trends" }, { "objectID": "index.html#sec-intro-tidyverse", @@ -39,28 +39,28 @@ "href": "ebird.html#sec-ebird-import", "title": "1  Best Practices for using eBird Data", "section": "1.3 Importing eBird data into R", - "text": "1.3 Importing eBird data into R\nThe previous step left us with two tab separated text files, one for the EBD (i.e. observation data) and one for the SED (i.e. checklist data). Start a new RStudio project and put the downloaded text files in the data/ sub-directory of the project directory.\nThe auk R package is specifically designed for working with eBird data. It contains the functions read_ebd() and read_sampling(), designed to import the EBD and SED, respectively, into R. First let’s import the checklist data (SED).\n\nlibrary(auk)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(lubridate)\nlibrary(sf)\nlibrary(tmap)\n\nf_sed <- \"data/ebd_BR-RS_fotfly_smp_relJun-2023_sampling.txt\"\nchecklists <- read_sampling(f_sed, unique = FALSE)\nglimpse(checklists)\n#> Rows: 34,005\n#> Columns: 30\n#> $ last_edited_date <chr> \"2021-02-15 15:28:32.796728\", \"2021-12-12 14…\n#> $ country <chr> \"Brazil\", \"Brazil\", \"Brazil\", \"Brazil\", \"Bra…\n#> $ country_code <chr> \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"B…\n#> $ state <chr> \"Rio Grande do Sul\", \"Rio Grande do Sul\", \"R…\n#> $ state_code <chr> \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\",…\n#> $ county <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ county_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ iba_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ bcr_code <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ usfws_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ atlas_block <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ locality <chr> \"UFRGS--Estação Experimental Agronômica\", \"C…\n#> $ locality_id <chr> \"L10057337\", \"L10637103\", \"L10637103\", \"L108…\n#> $ locality_type <chr> \"H\", \"P\", \"P\", \"P\", \"H\", \"H\", \"H\", \"P\", \"P\",…\n#> $ latitude <dbl> -30.1, -28.2, -28.2, -30.4, -32.6, -31.2, -3…\n#> $ longitude <dbl> -51.7, -51.7, -51.7, -51.5, -52.8, -50.8, -5…\n#> $ observation_date <date> 1993-03-22, 1998-12-11, 1999-12-27, 1990-01…\n#> $ time_observations_started <chr> \"08:00:00\", \"09:00:00\", \"09:00:00\", NA, NA, …\n#> $ observer_id <chr> \"obs203567\", \"obs1166183\", \"obs1166183\", \"ob…\n#> $ sampling_event_identifier <chr> \"S81646316\", \"S98801647\", \"S98802795\", \"S758…\n#> $ protocol_type <chr> \"Traveling\", \"Traveling\", \"Traveling\", \"Inci…\n#> $ protocol_code <chr> \"P22\", \"P22\", \"P22\", \"P20\", \"P20\", \"P62\", \"P…\n#> $ project_code <chr> \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\",…\n#> $ duration_minutes <int> 195, 120, 120, NA, NA, 90, NA, 80, NA, NA, N…\n#> $ effort_distance_km <dbl> 3.22, 2.00, 2.00, NA, NA, NA, NA, 4.00, NA, …\n#> $ effort_area_ha <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ number_observers <int> 2, 1, 1, NA, NA, 2, NA, 1, 1, 2, 1, 1, 1, 2,…\n#> $ all_species_reported <lgl> FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALS…\n#> $ group_identifier <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ trip_comments <chr> NA, \"lista feita por LB.\", \"lista feita por …\n\n\n\n\n\n\n\nCheckpoint\n\n\n\nTake some time to explore the variables in the checklist dataset. If you’re unsure about any of the variables, consult the metadata document that came with the data download (eBird_Basic_Dataset_Metadata_v1.14.pdf).\n\n\nNext we’ll import the observation data.\n\nf_ebd <- \"data/ebd_BR-RS_fotfly_smp_relJun-2023.txt\"\nobservations <- read_ebd(f_ebd, unique = FALSE, rollup = FALSE)\nglimpse(observations)\n#> Rows: 4,810\n#> Columns: 49\n#> $ global_unique_identifier <chr> \"URN:CornellLabOfOrnithology:EBIRD:OBS10006…\n#> $ last_edited_date <chr> \"2023-05-11 20:08:09.920625\", \"2022-08-04 0…\n#> $ taxonomic_order <dbl> 17062, 17062, 17062, 17062, 17062, 17062, 1…\n#> $ category <chr> \"species\", \"species\", \"species\", \"species\",…\n#> $ taxon_concept_id <chr> \"avibase-A2510EF4\", \"avibase-A2510EF4\", \"av…\n#> $ common_name <chr> \"Fork-tailed Flycatcher\", \"Fork-tailed Flyc…\n#> $ scientific_name <chr> \"Tyrannus savana\", \"Tyrannus savana\", \"Tyra…\n#> $ subspecies_common_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ subspecies_scientific_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ exotic_code <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ observation_count <chr> \"X\", \"1\", \"4\", \"1\", \"X\", \"X\", \"X\", \"X\", \"X\"…\n#> $ breeding_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ breeding_category <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ behavior_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ age_sex <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ country <chr> \"Brazil\", \"Brazil\", \"Brazil\", \"Brazil\", \"Br…\n#> $ country_code <chr> \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"…\n#> $ state <chr> \"Rio Grande do Sul\", \"Rio Grande do Sul\", \"…\n#> $ state_code <chr> \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\"…\n#> $ county <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ county_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ iba_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ bcr_code <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ usfws_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ atlas_block <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ locality <chr> \"São Francisco de Paula--listas históricas …\n#> $ locality_id <chr> \"L2862103\", \"L2453957\", \"L2312691\", \"L23126…\n#> $ locality_type <chr> \"H\", \"H\", \"P\", \"P\", \"P\", \"P\", \"P\", \"H\", \"H\"…\n#> $ latitude <dbl> -29.4, -31.3, -29.4, -29.4, -29.7, -28.2, -…\n#> $ longitude <dbl> -50.6, -51.0, -50.6, -50.6, -50.0, -51.7, -…\n#> $ observation_date <date> 1993-11-06, 1996-09-28, 1996-10-17, 1996-1…\n#> $ time_observations_started <chr> NA, \"07:00:00\", \"15:00:00\", \"06:00:00\", NA,…\n#> $ observer_id <chr> \"obsr477997\", \"obsr556494\", \"obsr293781\", \"…\n#> $ sampling_event_identifier <chr> \"S75217442\", \"S28837996\", \"S14886987\", \"S14…\n#> $ protocol_type <chr> \"Historical\", \"Historical\", \"Traveling\", \"T…\n#> $ protocol_code <chr> \"P62\", \"P62\", \"P22\", \"P22\", \"P20\", \"P22\", \"…\n#> $ project_code <chr> \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\"…\n#> $ duration_minutes <int> NA, 720, 120, 480, NA, 120, NA, NA, NA, 120…\n#> $ effort_distance_km <dbl> NA, NA, 1.61, 6.44, NA, 2.00, NA, NA, NA, 2…\n#> $ effort_area_ha <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ number_observers <int> NA, 2, 12, 12, 1, 1, 1, 2, 1, 1, 8, 8, 2, 2…\n#> $ all_species_reported <lgl> TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, …\n#> $ group_identifier <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ has_media <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F…\n#> $ approved <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, T…\n#> $ reviewed <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F…\n#> $ reason <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ trip_comments <chr> NA, NA, \"Victor Emanuel Nature Tours birdin…\n#> $ species_comments <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n\n\n\n\n\n\n\nCheckpoint\n\n\n\nTake some time to explore the variables in the observation dataset. Notice that the EBD duplicates many of the checklist-level variables from the SED.\n\n\nWhen we read the data into R you probably noticed we used unique = FALSE and rollup = FALSE. By default the read functions in auk perform two important pre-processing steps: combining duplicate shared checklists and taxonomic rollup. We intentionally turned off this functionality for the purposes of demonstration.\n\n1.3.1 Shared checklists\neBird allows users to share checklists with other eBirders in their group, for example this checklist is shared by 10 observers. These checklists can be identified by looking at the group_identifier variable, which assigns an ID connecting all checklists in the group.\n\nchecklists %>% \n filter(!is.na(group_identifier)) %>% \n arrange(group_identifier) %>% \n select(sampling_event_identifier, group_identifier)\n#> # A tibble: 8,632 × 2\n#> sampling_event_identifier group_identifier\n#> <chr> <chr> \n#> 1 S133745869 G10019405 \n#> 2 S134107963 G10019405 \n#> 3 S134468930 G10040061 \n#> 4 S134466265 G10040061 \n#> 5 S134466330 G10040062 \n#> 6 S134468944 G10040062 \n#> # ℹ 8,626 more rows\n\nChecklists with the same group_identifier provide duplicate information on the same birding event in the eBird database. For most analyses, it’s important to collapse these shared checklists down into a single checklist. This can be accomplished with the function auk_unique(), which retains only one independent copy of each checklist.\n\nchecklists_unique <- auk_unique(checklists, checklists_only = TRUE)\nnrow(checklists)\n#> [1] 34005\nnrow(checklists_unique)\n#> [1] 28930\n\nNotice that a new variable, checklist_id, was created that is set to group_identifier for shared checklists and sampling_event_identifier for non-shared checklists.\n\nhead(checklists_unique$checklist_id)\n#> [1] \"S81646316\" \"S98801647\" \"S98802795\" \"S7580857\" \"S7581039\" \"S28832587\"\ntail(checklists_unique$checklist_id)\n#> [1] \"G7592371\" \"G7592372\" \"G7573316\" \"G7594361\" \"G7633009\" \"G7603956\"\n\n\n\n1.3.2 Taxonomic rollup\neBird observations can be made at levels below species (e.g. subspecies) or above species (e.g. a bird that was identified as a duck, but the species could not be determined); however, for most uses we’ll want observations at the species level. This is especially true if we want to produce detection/non-detection data from complete checklists because “complete” only applies to species.\n\n\n\n\n\n\nTip\n\n\n\nIn the example dataset used for this workshop, these taxonomic issues don’t apply. We have requested Fork-tailed Flycatcher observations, so we haven’t received any observations for taxa above species, and Fork-tailed Flycatcher only has a single subspecies within Rio Grande do Sul. However, in many other situations, these taxonomic issues can be important.\n\n\nThe function auk_rollup() drops all observations not identifiable to a species and rolls up all observations reported below species to the species level.\n\nobservations_rollup <- auk_rollup(observations)\n# only one checklist is affected by this\nobservations %>% \n filter(sampling_event_identifier == \"S99335111\") %>% \n select(sampling_event_identifier, common_name, subspecies_common_name)\n#> # A tibble: 2 × 3\n#> sampling_event_identifier common_name subspecies_common_name \n#> <chr> <chr> <chr> \n#> 1 S99335111 Fork-tailed Flycatcher <NA> \n#> 2 S99335111 Fork-tailed Flycatcher Fork-tailed Flycatcher (sava…\nobservations_rollup %>% \n filter(sampling_event_identifier == \"S99335111\") %>% \n select(sampling_event_identifier, common_name)\n#> # A tibble: 1 × 2\n#> sampling_event_identifier common_name \n#> <chr> <chr> \n#> 1 S99335111 Fork-tailed Flycatcher" + "text": "1.3 Importing eBird data into R\nThe previous step left us with two tab separated text files, one for the EBD (i.e. observation data) and one for the SED (i.e. checklist data). Start a new RStudio project and put the downloaded text files in the data/ sub-directory of the project directory.\nThe auk R package is specifically designed for working with eBird data. It includes the functions read_ebd() and read_sampling() for importing the EBD and SED, respectively, into R. First let’s import the checklist data (SED).\n\nlibrary(auk)\nlibrary(dplyr)\nlibrary(ggplot2)\nlibrary(lubridate)\nlibrary(sf)\n\nf_sed <- \"data/ebd_BR-RS_fotfly_smp_relJun-2023_sampling.txt\"\nchecklists <- read_sampling(f_sed, unique = FALSE)\nglimpse(checklists)\n#> Rows: 34,005\n#> Columns: 30\n#> $ last_edited_date <chr> \"2021-02-15 15:28:32.796728\", \"2021-12-12 14…\n#> $ country <chr> \"Brazil\", \"Brazil\", \"Brazil\", \"Brazil\", \"Bra…\n#> $ country_code <chr> \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"B…\n#> $ state <chr> \"Rio Grande do Sul\", \"Rio Grande do Sul\", \"R…\n#> $ state_code <chr> \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\",…\n#> $ county <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ county_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ iba_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ bcr_code <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ usfws_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ atlas_block <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ locality <chr> \"UFRGS--Estação Experimental Agronômica\", \"C…\n#> $ locality_id <chr> \"L10057337\", \"L10637103\", \"L10637103\", \"L108…\n#> $ locality_type <chr> \"H\", \"P\", \"P\", \"P\", \"H\", \"H\", \"H\", \"P\", \"P\",…\n#> $ latitude <dbl> -30.1, -28.2, -28.2, -30.4, -32.6, -31.2, -3…\n#> $ longitude <dbl> -51.7, -51.7, -51.7, -51.5, -52.8, -50.8, -5…\n#> $ observation_date <date> 1993-03-22, 1998-12-11, 1999-12-27, 1990-01…\n#> $ time_observations_started <chr> \"08:00:00\", \"09:00:00\", \"09:00:00\", NA, NA, …\n#> $ observer_id <chr> \"obs203567\", \"obs1166183\", \"obs1166183\", \"ob…\n#> $ sampling_event_identifier <chr> \"S81646316\", \"S98801647\", \"S98802795\", \"S758…\n#> $ protocol_type <chr> \"Traveling\", \"Traveling\", \"Traveling\", \"Inci…\n#> $ protocol_code <chr> \"P22\", \"P22\", \"P22\", \"P20\", \"P20\", \"P62\", \"P…\n#> $ project_code <chr> \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\",…\n#> $ duration_minutes <int> 195, 120, 120, NA, NA, 90, NA, 80, NA, NA, N…\n#> $ effort_distance_km <dbl> 3.22, 2.00, 2.00, NA, NA, NA, NA, 4.00, NA, …\n#> $ effort_area_ha <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ number_observers <int> 2, 1, 1, NA, NA, 2, NA, 1, 1, 2, 1, 1, 1, 2,…\n#> $ all_species_reported <lgl> FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALS…\n#> $ group_identifier <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ trip_comments <chr> NA, \"lista feita por LB.\", \"lista feita por …\n\n\n\n\n\n\n\nCheckpoint\n\n\n\nTake some time to explore the variables in the checklist dataset. If you’re unsure about any of the variables, consult the metadata document that came with the data download (eBird_Basic_Dataset_Metadata_v1.14.pdf).\n\n\nNext, let’s import the observation data.\n\nf_ebd <- \"data/ebd_BR-RS_fotfly_smp_relJun-2023.txt\"\nobservations <- read_ebd(f_ebd, unique = FALSE, rollup = FALSE)\nglimpse(observations)\n#> Rows: 4,810\n#> Columns: 49\n#> $ global_unique_identifier <chr> \"URN:CornellLabOfOrnithology:EBIRD:OBS10006…\n#> $ last_edited_date <chr> \"2023-05-11 20:08:09.920625\", \"2022-08-04 0…\n#> $ taxonomic_order <dbl> 17062, 17062, 17062, 17062, 17062, 17062, 1…\n#> $ category <chr> \"species\", \"species\", \"species\", \"species\",…\n#> $ taxon_concept_id <chr> \"avibase-A2510EF4\", \"avibase-A2510EF4\", \"av…\n#> $ common_name <chr> \"Fork-tailed Flycatcher\", \"Fork-tailed Flyc…\n#> $ scientific_name <chr> \"Tyrannus savana\", \"Tyrannus savana\", \"Tyra…\n#> $ subspecies_common_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ subspecies_scientific_name <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ exotic_code <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ observation_count <chr> \"X\", \"1\", \"4\", \"1\", \"X\", \"X\", \"X\", \"X\", \"X\"…\n#> $ breeding_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ breeding_category <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ behavior_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ age_sex <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ country <chr> \"Brazil\", \"Brazil\", \"Brazil\", \"Brazil\", \"Br…\n#> $ country_code <chr> \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"…\n#> $ state <chr> \"Rio Grande do Sul\", \"Rio Grande do Sul\", \"…\n#> $ state_code <chr> \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\"…\n#> $ county <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ county_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ iba_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ bcr_code <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ usfws_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ atlas_block <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ locality <chr> \"São Francisco de Paula--listas históricas …\n#> $ locality_id <chr> \"L2862103\", \"L2453957\", \"L2312691\", \"L23126…\n#> $ locality_type <chr> \"H\", \"H\", \"P\", \"P\", \"P\", \"P\", \"P\", \"H\", \"H\"…\n#> $ latitude <dbl> -29.4, -31.3, -29.4, -29.4, -29.7, -28.2, -…\n#> $ longitude <dbl> -50.6, -51.0, -50.6, -50.6, -50.0, -51.7, -…\n#> $ observation_date <date> 1993-11-06, 1996-09-28, 1996-10-17, 1996-1…\n#> $ time_observations_started <chr> NA, \"07:00:00\", \"15:00:00\", \"06:00:00\", NA,…\n#> $ observer_id <chr> \"obsr477997\", \"obsr556494\", \"obsr293781\", \"…\n#> $ sampling_event_identifier <chr> \"S75217442\", \"S28837996\", \"S14886987\", \"S14…\n#> $ protocol_type <chr> \"Historical\", \"Historical\", \"Traveling\", \"T…\n#> $ protocol_code <chr> \"P62\", \"P62\", \"P22\", \"P22\", \"P20\", \"P22\", \"…\n#> $ project_code <chr> \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\"…\n#> $ duration_minutes <int> NA, 720, 120, 480, NA, 120, NA, NA, NA, 120…\n#> $ effort_distance_km <dbl> NA, NA, 1.61, 6.44, NA, 2.00, NA, NA, NA, 2…\n#> $ effort_area_ha <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ number_observers <int> NA, 2, 12, 12, 1, 1, 1, 2, 1, 1, 8, 8, 2, 2…\n#> $ all_species_reported <lgl> TRUE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, …\n#> $ group_identifier <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ has_media <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F…\n#> $ approved <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, T…\n#> $ reviewed <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, F…\n#> $ reason <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n#> $ trip_comments <chr> NA, NA, \"Victor Emanuel Nature Tours birdin…\n#> $ species_comments <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…\n\n\n\n\n\n\n\nCheckpoint\n\n\n\nTake some time to explore the variables in the observation dataset. Notice that the EBD duplicates many of the checklist-level variables from the SED.\n\n\nWhen we read the data into R, we used unique = FALSE and rollup = FALSE. By default the read functions in auk perform two important pre-processing steps: combining duplicate shared checklists and taxonomic rollup. We intentionally turned off this functionality for the purposes of demonstration.\n\n1.3.1 Shared checklists\neBird allows users to share checklists with other eBirders in their group, for example this checklist is shared by 10 observers. These checklists can be identified by looking at the group_identifier variable, which assigns an ID connecting all checklists in the group.\n\nchecklists %>% \n filter(!is.na(group_identifier)) %>% \n arrange(group_identifier) %>% \n select(sampling_event_identifier, group_identifier)\n#> # A tibble: 8,632 × 2\n#> sampling_event_identifier group_identifier\n#> <chr> <chr> \n#> 1 S133745869 G10019405 \n#> 2 S134107963 G10019405 \n#> 3 S134468930 G10040061 \n#> 4 S134466265 G10040061 \n#> 5 S134466330 G10040062 \n#> 6 S134468944 G10040062 \n#> # ℹ 8,626 more rows\n\nChecklists with the same group_identifier provide duplicate information on the same birding event in the eBird database. For most analyses, it’s important to collapse these shared checklists down into a single checklist. This can be accomplished with the function auk_unique(), which retains only one independent copy of each checklist.\n\nchecklists_unique <- auk_unique(checklists, checklists_only = TRUE)\nnrow(checklists)\n#> [1] 34005\nnrow(checklists_unique)\n#> [1] 28930\n\nNotice that a new variable, checklist_id, was created that is set to group_identifier for shared checklists and sampling_event_identifier for non-shared checklists.\n\nhead(checklists_unique$checklist_id)\n#> [1] \"S81646316\" \"S98801647\" \"S98802795\" \"S7580857\" \"S7581039\" \"S28832587\"\ntail(checklists_unique$checklist_id)\n#> [1] \"G7592371\" \"G7592372\" \"G7573316\" \"G7594361\" \"G7633009\" \"G7603956\"\n\n\n\n\n\n\n\nTip\n\n\n\nCurious when checklists and observers contributed to a shared checklist after it has been collapsed? The sampling_event_identifier and observer_id contain comma-separated lists of all checklists and observers that went into the shared checklists.\n\nchecklists_unique %>% \n filter(checklist_id == \"G10019405\") %>% \n select(checklist_id, group_identifier, sampling_event_identifier, observer_id)\n#> # A tibble: 1 × 4\n#> checklist_id group_identifier sampling_event_identifier observer_id \n#> <chr> <chr> <chr> <chr> \n#> 1 G10019405 G10019405 S133745869,S134107963 obs1481597,obs567598\n\n\n\n\n\n1.3.2 Taxonomic rollup\neBird observations can be made at levels below species (e.g. subspecies) or above species (e.g. a bird that was identified as a duck, but the species could not be determined); however, for most uses we’ll want observations at the species level. This is especially true if we want to produce detection/non-detection data from complete checklists because “complete” only applies at the species level.\n\n\n\n\n\n\nTip\n\n\n\nIn the example dataset used for this workshop, these taxonomic issues don’t apply. We have requested Fork-tailed Flycatcher observations, so we haven’t received any observations for taxa above species, and Fork-tailed Flycatcher only has a single subspecies within Rio Grande do Sul. However, in many other situations, these taxonomic issues can be important.\n\n\nThe function auk_rollup() drops all observations not identifiable to a species and rolls up all observations reported below species to the species level.\n\nobservations_rollup <- auk_rollup(observations)\n# only one checklist is affected by this\nobservations %>% \n filter(sampling_event_identifier == \"S99335111\") %>% \n select(sampling_event_identifier, common_name, subspecies_common_name, \n observation_count)\n#> # A tibble: 2 × 4\n#> sampling_event_identifier common_name subspecies_common_name observation_count\n#> <chr> <chr> <chr> <chr> \n#> 1 S99335111 Fork-taile… <NA> X \n#> 2 S99335111 Fork-taile… Fork-tailed Flycatche… 1\nobservations_rollup %>% \n filter(sampling_event_identifier == \"S99335111\") %>% \n select(sampling_event_identifier, common_name,\n observation_count)\n#> # A tibble: 1 × 3\n#> sampling_event_identifier common_name observation_count\n#> <chr> <chr> <chr> \n#> 1 S99335111 Fork-tailed Flycatcher X\n\n\n\n\n\n\n\nTip\n\n\n\nIf multiple taxa on a single checklist roll up to the same species, auk_rollup() attempts to combine them intelligently. If each observation has a count, those counts are added together, but if any of the observations is missing a count (i.e. the count is “X”) the combined observation is also assigned an “X”." }, { "objectID": "ebird.html#sec-ebird-zf", "href": "ebird.html#sec-ebird-zf", "title": "1  Best Practices for using eBird Data", "section": "1.4 Generating detection/non-detection data", - "text": "1.4 Generating detection/non-detection data\nComplete eBird checklists are extremely valuable because, for all species that weren’t reported, we can infer counts of 0. This allows us to convert eBird from presence only data to detection/non-detection data, which allows for much more robust analyses. Note that we don’t use the term presence/absence data here because a non-detection doesn’t necessarily imply the species was absent, only that observer didn’t detect and identify it.\nWe refer to the process of producing detection/non-detection data as “zero-filling” the eBird data because we’re filling in the missing zeros. We’ll read the eBird data into R again, filter to only complete checklists, then use the function auk_zerofill() to generate detection/non-detection data. Note that shared checklists are combined and taxonomic rollup is performed by default when using the read_*() functions from auk.\n\n# import checklist data\nchecklists <- read_sampling(f_sed) %>% \n # subset to complete checklists\n filter(all_species_reported)\n# import observation data\nobservations <- read_ebd(f_ebd) %>% \n # subset to complete checklists\n filter(all_species_reported)\n# zero-fill to produce detection/non-detection data\nzf <- auk_zerofill(observations, checklists, collapse = TRUE)\nglimpse(zf)\n#> Rows: 22,838\n#> Columns: 38\n#> $ checklist_id <chr> \"S98801647\", \"S98802795\", \"S91164181\", \"S114…\n#> $ last_edited_date <chr> \"2021-12-12 14:52:43.115062\", \"2022-10-25 03…\n#> $ country <chr> \"Brazil\", \"Brazil\", \"Brazil\", \"Brazil\", \"Bra…\n#> $ country_code <chr> \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"B…\n#> $ state <chr> \"Rio Grande do Sul\", \"Rio Grande do Sul\", \"R…\n#> $ state_code <chr> \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\",…\n#> $ county <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ county_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ iba_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ bcr_code <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ usfws_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ atlas_block <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ locality <chr> \"Capela Sagrado Coração de Jesus,RS,BR\", \"Ca…\n#> $ locality_id <chr> \"L10637103\", \"L10637103\", \"L15616802\", \"L165…\n#> $ locality_type <chr> \"P\", \"P\", \"P\", \"P\", \"H\", \"P\", \"P\", \"P\", \"P\",…\n#> $ latitude <dbl> -28.2, -28.2, -29.3, -29.3, -29.5, -29.3, -3…\n#> $ longitude <dbl> -51.7, -51.7, -49.7, -49.7, -50.6, -50.3, -5…\n#> $ observation_date <date> 1998-12-11, 1999-12-27, 1989-12-27, 1992-04…\n#> $ time_observations_started <chr> \"09:00:00\", \"09:00:00\", \"07:30:00\", NA, NA, …\n#> $ observer_id <chr> \"obs1166183\", \"obs1166183\", \"obs501289\", \"ob…\n#> $ sampling_event_identifier <chr> \"S98801647\", \"S98802795\", \"S91164181\", \"S114…\n#> $ protocol_type <chr> \"Traveling\", \"Traveling\", \"Traveling\", \"Hist…\n#> $ protocol_code <chr> \"P22\", \"P22\", \"P22\", \"P62\", \"P62\", \"P22\", \"P…\n#> $ project_code <chr> \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\",…\n#> $ duration_minutes <int> 120, 120, 80, NA, NA, 180, NA, NA, 120, 480,…\n#> $ effort_distance_km <dbl> 2.000, 2.000, 4.000, NA, NA, 5.000, NA, NA, …\n#> $ effort_area_ha <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ number_observers <int> 1, 1, 1, 1, 2, 1, 3, 3, 12, 12, 12, 2, 1, NA…\n#> $ all_species_reported <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…\n#> $ group_identifier <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ trip_comments <chr> \"lista feita por LB.\", \"lista feita por LB.\"…\n#> $ scientific_name <chr> \"Tyrannus savana\", \"Tyrannus savana\", \"Tyran…\n#> $ breeding_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ breeding_category <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ behavior_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ age_sex <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ observation_count <chr> \"X\", \"X\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"4\",…\n#> $ species_observed <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALS…\n\nThe observation_count variable has true counts as well as “X”s, which indicate that the species was detected but the number of individuals was not counted. auk_zerofill() adds a new binary column, species_observed, indicating whether or not the species was detected.\n\nselect(zf, observation_count, species_observed) %>% \n head(10)\n#> # A tibble: 10 × 2\n#> observation_count species_observed\n#> <chr> <lgl> \n#> 1 X TRUE \n#> 2 X TRUE \n#> 3 0 FALSE \n#> 4 0 FALSE \n#> 5 0 FALSE \n#> 6 0 FALSE \n#> # ℹ 4 more rows\n\nLet’s convert the “X”s to NAs and transform observation_count to an integer variable.\n\nzf$observation_count <- if_else(zf$observation_count == \"X\", \n NA_character_, zf$observation_count) %>% \n as.integer()\nselect(zf, observation_count, species_observed) %>% \n head(10)\n#> # A tibble: 10 × 2\n#> observation_count species_observed\n#> <int> <lgl> \n#> 1 NA TRUE \n#> 2 NA TRUE \n#> 3 0 FALSE \n#> 4 0 FALSE \n#> 5 0 FALSE \n#> 6 0 FALSE \n#> # ℹ 4 more rows" + "text": "1.4 Generating detection/non-detection data\nComplete eBird checklists are extremely valuable because, for all species that weren’t reported, we can infer counts of 0. This allows us to convert eBird from presence only data to detection/non-detection data, which allows for much more robust analyses. Note that we don’t use the term presence/absence data here because a non-detection doesn’t necessarily imply the species was absent, only that the observer didn’t detect and identify it.\nWe refer to the process of producing detection/non-detection data as “zero-filling” the eBird data because we’re filling in the missing zeros. We’ll read the eBird data into R again, filter to only complete checklists, then use the function auk_zerofill() to generate detection/non-detection data. Note that shared checklists are combined and taxonomic rollup is performed by default when using the read_*() functions from auk.\n\n# import checklist data\nchecklists <- read_sampling(f_sed) %>% \n # subset to complete checklists\n filter(all_species_reported)\n# import observation data\nobservations <- read_ebd(f_ebd) %>% \n # subset to complete checklists\n filter(all_species_reported)\n# zero-fill to produce detection/non-detection data\nzf <- auk_zerofill(observations, checklists, collapse = TRUE)\nglimpse(zf)\n#> Rows: 22,838\n#> Columns: 38\n#> $ checklist_id <chr> \"S98801647\", \"S98802795\", \"S91164181\", \"S114…\n#> $ last_edited_date <chr> \"2021-12-12 14:52:43.115062\", \"2022-10-25 03…\n#> $ country <chr> \"Brazil\", \"Brazil\", \"Brazil\", \"Brazil\", \"Bra…\n#> $ country_code <chr> \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"BR\", \"B…\n#> $ state <chr> \"Rio Grande do Sul\", \"Rio Grande do Sul\", \"R…\n#> $ state_code <chr> \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\", \"BR-RS\",…\n#> $ county <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ county_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ iba_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ bcr_code <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ usfws_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ atlas_block <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ locality <chr> \"Capela Sagrado Coração de Jesus,RS,BR\", \"Ca…\n#> $ locality_id <chr> \"L10637103\", \"L10637103\", \"L15616802\", \"L165…\n#> $ locality_type <chr> \"P\", \"P\", \"P\", \"P\", \"H\", \"P\", \"P\", \"P\", \"P\",…\n#> $ latitude <dbl> -28.2, -28.2, -29.3, -29.3, -29.5, -29.3, -3…\n#> $ longitude <dbl> -51.7, -51.7, -49.7, -49.7, -50.6, -50.3, -5…\n#> $ observation_date <date> 1998-12-11, 1999-12-27, 1989-12-27, 1992-04…\n#> $ time_observations_started <chr> \"09:00:00\", \"09:00:00\", \"07:30:00\", NA, NA, …\n#> $ observer_id <chr> \"obs1166183\", \"obs1166183\", \"obs501289\", \"ob…\n#> $ sampling_event_identifier <chr> \"S98801647\", \"S98802795\", \"S91164181\", \"S114…\n#> $ protocol_type <chr> \"Traveling\", \"Traveling\", \"Traveling\", \"Hist…\n#> $ protocol_code <chr> \"P22\", \"P22\", \"P22\", \"P62\", \"P62\", \"P22\", \"P…\n#> $ project_code <chr> \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\", \"EBIRD\",…\n#> $ duration_minutes <int> 120, 120, 80, NA, NA, 180, NA, NA, 120, 480,…\n#> $ effort_distance_km <dbl> 2.000, 2.000, 4.000, NA, NA, 5.000, NA, NA, …\n#> $ effort_area_ha <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ number_observers <int> 1, 1, 1, 1, 2, 1, 3, 3, 12, 12, 12, 2, 1, NA…\n#> $ all_species_reported <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TR…\n#> $ group_identifier <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ trip_comments <chr> \"lista feita por LB.\", \"lista feita por LB.\"…\n#> $ scientific_name <chr> \"Tyrannus savana\", \"Tyrannus savana\", \"Tyran…\n#> $ breeding_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ breeding_category <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ behavior_code <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ age_sex <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …\n#> $ observation_count <chr> \"X\", \"X\", \"0\", \"0\", \"0\", \"0\", \"0\", \"0\", \"4\",…\n#> $ species_observed <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALS…\n\nThe observation_count variable has true counts as well as “X”s, which indicate that the species was detected but the number of individuals was not counted. auk_zerofill() adds a new binary column, species_observed, indicating whether or not the species was detected.\n\nselect(zf, observation_count, species_observed) %>% \n head(10)\n#> # A tibble: 10 × 2\n#> observation_count species_observed\n#> <chr> <lgl> \n#> 1 X TRUE \n#> 2 X TRUE \n#> 3 0 FALSE \n#> 4 0 FALSE \n#> 5 0 FALSE \n#> 6 0 FALSE \n#> # ℹ 4 more rows\n\nLet’s convert the “X”s to NAs and transform observation_count to an integer variable.\n\nzf$observation_count <- if_else(zf$observation_count == \"X\", \n NA_character_, zf$observation_count) %>% \n as.integer()\nselect(zf, observation_count, species_observed) %>% \n head(10)\n#> # A tibble: 10 × 2\n#> observation_count species_observed\n#> <int> <lgl> \n#> 1 NA TRUE \n#> 2 NA TRUE \n#> 3 0 FALSE \n#> 4 0 FALSE \n#> 5 0 FALSE \n#> 6 0 FALSE \n#> # ℹ 4 more rows" }, { "objectID": "ebird.html#sec-ebird-filtering", "href": "ebird.html#sec-ebird-filtering", "title": "1  Best Practices for using eBird Data", "section": "1.5 Filtering data", - "text": "1.5 Filtering data\nNow that you have a detection/non-detection dataset, it’s likely that you want to do something with it. For example, you may want to make a map, use the eBird data to identify priority areas for a species, or train a species distribution model. Regardless of the specific application, it’s likely that some amount of filtering of the data is required first. Some of the ways you may want to filter eBird data include:\n\nTemporal filtering: filter the data to a specific range of years or to a specific time of year.\nSpatial filtering: filter the data to focus on a specific region, e.g. a protected area.\nIncreasing precision: some eBird checklists are quite long in distance or duration leading to spatial or temporal imprecision. By removing longer checklists we can increase the spatial precision of the dataset.\nReducing variation in effort: unlike structured scientific surveys, data can be submitted to eBird using a variety of protocols and there is significant variation in effort between checklists in the eBird dataset. Variation in protocol and effort leads to variation in detectability (more effort generally leads to higher detectability). We can choose to impose more structure on the eBird dataset by filtering to reduce variation in protocol and effort.\n\nThe specific filtering you apply will depend on how you intend to use the eBird data. However, for the sake of this example, let’s filter the eBird data to only traveling and stationary checklists from 2013-2022 that are less than 6 hours in duration and 10km in length.\n\nzf_filtered <- zf %>% \n filter(year(observation_date) >= 2013, year(observation_date) <= 2022,\n protocol_type %in% c(\"Traveling\", \"Stationary\"),\n duration_minutes < 6 * 60,\n effort_distance_km < 10 | protocol_type == \"Stationary\")\nnrow(zf)\n#> [1] 22838\nnrow(zf_filtered)\n#> [1] 16526\n\nWe reduced the number of checklists by 6,312, but the checklists remaining are of higher quality.\nFinally, many of the columns in this data frame are unnecessary or redundant, so we’ll select only the columns we need.\n\nchecklists <- zf_filtered %>% \n select(checklist_id, \n latitude, longitude,\n observation_date, time_observations_started,\n protocol_type,\n duration_minutes, effort_distance_km, number_observers,\n observation_count, species_observed)" + "text": "1.5 Filtering data\nNow that you have a detection/non-detection dataset, it’s likely that you want to do something with it. For example, you may want to make a map, identify priority areas for a species, or train a species distribution model. Regardless of the specific application, it’s likely that some amount of filtering of the data is required first. Some of the ways you may want to filter eBird data include:\n\nTemporal filtering: filter the data to a specific range of years or to a specific time of year.\nSpatial filtering: filter the data to focus on a specific region, e.g. a protected area.\nIncreasing precision: some eBird checklists are quite long in distance or duration leading to spatial or temporal imprecision. By removing longer checklists we can increase the spatial precision of the dataset.\nReducing variation in effort: unlike structured scientific surveys, data can be submitted to eBird using a variety of protocols and there is significant variation in effort between checklists in the eBird dataset. Variation in protocol and effort leads to variation in detectability (more effort generally leads to higher detectability). We can choose to impose more structure on the eBird dataset by filtering to reduce variation in protocol and effort.\n\nThe specific filtering you apply will depend on how you intend to use the eBird data. However, for the sake of this example, let’s filter the eBird data to only traveling and stationary checklists from 2013-2022 that are less than 6 hours in duration and 10 km in length.\n\nzf_filtered <- zf %>% \n filter(year(observation_date) >= 2013, year(observation_date) <= 2022,\n protocol_type %in% c(\"Traveling\", \"Stationary\"),\n duration_minutes < 6 * 60,\n effort_distance_km < 10 | protocol_type == \"Stationary\")\nnrow(zf)\n#> [1] 22838\nnrow(zf_filtered)\n#> [1] 16526\n\nWe reduced the number of checklists by 6,312, but the checklists remaining are of higher quality.\nFinally, many of the columns in this data frame are unnecessary or redundant, so we’ll select only the columns we need.\n\nchecklists_zf <- zf_filtered %>% \n select(checklist_id, \n latitude, longitude,\n observation_date, time_observations_started,\n protocol_type,\n duration_minutes, effort_distance_km, number_observers,\n observation_count, species_observed)" }, { "objectID": "ebird.html#sec-ebird-applications", "href": "ebird.html#sec-ebird-applications", "title": "1  Best Practices for using eBird Data", "section": "1.6 Applications", - "text": "1.6 Applications\n\n1.6.1 Detection frequency\nThe simplest thing we can do with these eBird observations is estimate the frequency of detection of Fork-tailed Flycatcher within Rio Grande do Sul. This is the proportion of eBird checklists in the region that detected the species. species_observed is a binary TRUE/FALSE column indicating if a species was detected and R treats TRUE as 1 and FALSE as 0, so we can take the average of this column to get the detection frequency.\n\nmean(checklists$species_observed)\n#> [1] 0.15\n\nSo, Fork-tailed Flycatcher is fairly common within this region with 15% of checklists detecting the species. Detection frequency can be used to compare the prevalence of a species between regions or over time. For example, Fork-tailed Flycatcher is migratory, so let’s look at how detection frequency changes over the months of the year.\n\nmonthly_detection <- checklists %>% \n mutate(month = month(observation_date)) %>% \n group_by(month) %>% \n summarize(detection_frequency = mean(species_observed))\n\n# plot monthly detection frequency\nggplot(monthly_detection) +\n aes(x = month, y = detection_frequency) +\n geom_line() +\n geom_point() +\n scale_x_continuous(breaks = 1:12) +\n scale_y_continuous(labels = scales::percent) +\n labs(x = \"Month of year\",\n y = \"Detection frequency\",\n title = \"Monthly detection frequency for Fork-tailed Flycatcher\",\n subtitle = \"Rio Grande do Sul, Brazil\")\n\n\n\n\n\n\n\n\nBased on this chart, Fork-tailed Flycatcher appears to arrive in Rio Grande do Sul in September and depart in February.\n\n\n\n\n\n\nExercise\n\n\n\nUse a similar technique to calculate and plot the monthly number of eBird checklists in Rio Grande do Sul. Are there differences between months? If so, what does this tell you about using number of detections from presence-only as a measure of prevalence?\n\n\n\n\n\n\n\n\nSolution\n\n\n\n\n\nThere is significant variability in checklist submissions per month, with twice as many checklists in February as October. If we were to look at the monthly number of detections from presence-only data we would expect more detections in months with more checklists even if the prevalence of Fork-tailed Flycatcher is the same.\n\nmonthly_checklists <- count(checklists, month = month(observation_date))\n\n# plot monthly number of checklists\nggplot(monthly_checklists) +\n aes(x = month, y = n) +\n geom_line() +\n geom_point() +\n scale_x_continuous(breaks = 1:12) +\n scale_y_continuous(limits = c(0, NA), labels = scales::comma) +\n labs(x = \"Month of year\",\n y = \"# of eBird checklists\",\n title = \"Monthly eBird checklist submissions in Rio Grande do Sul\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n1.6.2 Converting to spatial format\nMany applications of eBird data require converting the data into an explicitly spatial format, for example, to make a map or subset the observations to only those within a polygon for a protected area. The R package sf can be use to represent spatial points, lines, and polygons in R. We can convert the data frame of checklists to an sf object using the latitude and longitude.\n\nchecklists_sf <- st_as_sf(checklists, coords = c(\"longitude\", \"latitude\"),\n # 4326 is the code for an unprojected lon/lat\n # coordiante reference system\n crs = 4326)\nprint(checklists_sf)\n#> Simple feature collection with 16526 features and 9 fields\n#> Geometry type: POINT\n#> Dimension: XY\n#> Bounding box: xmin: -57.6 ymin: -35 xmax: -48.7 ymax: -27.1\n#> Geodetic CRS: WGS 84\n#> # A tibble: 16,526 × 10\n#> checklist_id observation_date time_observations_started protocol_type\n#> * <chr> <date> <chr> <chr> \n#> 1 S117444976 2013-05-18 07:47:00 Traveling \n#> 2 S117215031 2013-04-14 07:53:00 Traveling \n#> 3 S53740464 2013-01-11 08:00:00 Traveling \n#> 4 S16023053 2013-12-14 17:30:00 Stationary \n#> 5 S15844084 2013-12-03 13:00:00 Traveling \n#> 6 S117214399 2013-03-16 07:36:00 Traveling \n#> # ℹ 16,520 more rows\n#> # ℹ 6 more variables: duration_minutes <int>, effort_distance_km <dbl>,\n#> # number_observers <int>, observation_count <int>, species_observed <lgl>,\n#> # geometry <POINT [°]>\n\nImagine that we’re only interested in observations from within 50 km of Gramado, Brazil (29.375°S, 50.876°W). We can use sf to generate a circle of radium 50 km centered on Gramado, then subset observations to only those falling within the circle.\n\ngramado_point <- st_sfc(st_point(c(-50.876, -29.375)), crs = 4326)\n# 50km = 50,000m\ngramado_circle <- st_buffer(gramado_point, dist = 50000)\nchecklists_gramado <- checklists_sf[gramado_circle, ]\n\nWe could now, for example, calculate the detection frequency in the area around Gramado.\n\nmean(checklists_gramado$species_observed)\n#> [1] 0.097\n\nFinally, in many cases, it may be more convenient to work with eBird data outside of R in a GIS such as QGIS, a free and open source GIS. We can export the Fork-tailed Flycatcher observations to a GeoPackage using sf.\n\nwrite_sf(checklists_sf, \"data/fotfly-ebird_br-rs.gpkg\",\n layer = \"fotfly_observations\")\n\n\n\n\n\n\n\nTip\n\n\n\nGeoPackage is a modern, open source alternative to the shapefile format for storing spatial data. GeoPackages avoid many of the problems and limitations associated with shapefiles, and they are much more efficient than shapefiles. The online ArcGIS documentation provides instructions for how to open a GeoPackage in ArcGIS.\n\n\n\n\n1.6.3 Mapping\nNow that we have the data in a spatial format, we can produce a map of the Fork-tailed Flycatcher detection/non-detection data. For complex, highly customized maps, we recommend using a GIS such as QGIS that’s specifically designed for cartography. However, it’s possible to make a quick, simple map in R using the tmap package.\nWe’ll start by loading polygons defining coutry and state borders, which will provide contextual information for our map. These polygons come from Natural Earth, and excellent source of global, attribution free spatial data. The R package rnaturalearth provides access to Natural Earth data within R; however, for convenience we’ve provided the necessary layers in the file data/gis-data.gpkg included in the data package for this workshop. We’ll also project everything to an equal area projection center on Rio Grande do Sul.\n\ncrs <- st_crs(\"+proj=laea +lat_0=-30 +lon_0=-50\")\n\n# polygons from natural earth\nne_land <- read_sf(\"data/gis-data.gpkg\", \"ne_land\") %>% \n st_transform(crs = crs)\nne_country_lines <- read_sf(\"data/gis-data.gpkg\", \"ne_country_lines\") %>% \n st_transform(crs = crs)\nrgds_boundary <- read_sf(\"data/gis-data.gpkg\", \"ne_states\") %>% \n filter(state_code == \"BR-RS\") %>% \n st_transform(crs = crs)\n\n# transform ebird data to equal area projection\nchecklists_proj <- st_transform(checklists_sf, crs = crs)\n\n\npar(mar = c(0.25, 0.25, 0.25, 0.25))\n\n# set up plot area\nplot(st_geometry(checklists_proj), col = NA)\n\n# contextual gis data\nplot(ne_land, col = \"#dddddd\", border = \"#888888\", lwd = 0.5, add = TRUE)\nplot(rgds_boundary, col = \"#cccccc\", border = NA, add = TRUE)\n#> Warning in plot.sf(rgds_boundary, col = \"#cccccc\", border = NA, add = TRUE):\n#> ignoring all but the first attribute\nplot(ne_country_lines, col = \"#ffffff\", lwd = 1.5, add = TRUE)\n\n# ebird observations\n# not detected\nplot(filter(checklists_proj, !species_observed),\n pch = 19, cex = 0.2, col = alpha(\"#555555\", 0.5),\n add = TRUE)\n#> Warning in plot.sf(filter(checklists_proj, !species_observed), pch = 19, :\n#> ignoring all but the first attribute\n# detected\nplot(filter(checklists_proj, species_observed),\n pch = 19, cex = 0.3, col = alpha(\"#4daf4a\", 1),\n add = TRUE)\n#> Warning in plot.sf(filter(checklists_proj, species_observed), pch = 19, :\n#> ignoring all but the first attribute\n# legend\nlegend(\"bottomright\", bty = \"n\",\n col = c(\"#555555\", \"#4daf4a\"),\n legend = c(\"eBird checklists\", \"Fork-tailed Flycatcher sightings\"),\n pch = 19)\nbox()\npar(new = TRUE, mar = c(0, 0, 3, 0))\ntitle(\"Fork-tailed Flycatcher eBird Observations\\n2013-2022\")" + "text": "1.6 Applications\n\n1.6.1 Detection frequency\nThe simplest thing we can do with these eBird observations is estimate the frequency of detection of Fork-tailed Flycatcher within Rio Grande do Sul. This is the proportion of eBird checklists in the region that detected the species. species_observed is a binary TRUE/FALSE column indicating if a species was detected and R treats TRUE as 1 and FALSE as 0, so we can take the average of this column to get the detection frequency.\n\nmean(checklists_zf$species_observed)\n#> [1] 0.15\n\nSo, Fork-tailed Flycatcher is fairly common within this region with 15% of checklists detecting the species. Detection frequency can be used to compare the prevalence of a species between regions or over time. For example, Fork-tailed Flycatcher is migratory, so let’s look at how detection frequency changes over the months of the year.\n\nmonthly_detection <- checklists_zf %>% \n mutate(month = month(observation_date)) %>% \n group_by(month) %>% \n summarize(detection_frequency = mean(species_observed))\n\n# plot monthly detection frequency\nggplot(monthly_detection) +\n aes(x = month, y = detection_frequency) +\n geom_line() +\n geom_point() +\n scale_x_continuous(breaks = 1:12) +\n scale_y_continuous(labels = scales::percent) +\n labs(x = \"Month of year\",\n y = \"Detection frequency\",\n title = \"Monthly detection frequency for Fork-tailed Flycatcher\",\n subtitle = \"Rio Grande do Sul, Brazil\")\n\n\n\n\n\n\n\n\nBased on this chart, Fork-tailed Flycatcher appears to arrive in Rio Grande do Sul in September and depart in February.\n\n\n\n\n\n\nExercise\n\n\n\nUse a similar technique to calculate and plot the monthly number of eBird checklists in Rio Grande do Sul. Are there differences between months? If so, what does this tell you about using number of detections from presence-only as a measure of prevalence?\n\n\n\n\n\n\n\n\nSolution\n\n\n\n\n\nThere is significant variability in checklist submissions per month, with twice as many checklists in February as October. If we were to look at the monthly number of detections from presence-only data we would expect more detections in months with more checklists even if the prevalence of Fork-tailed Flycatcher is the same.\n\nmonthly_checklists <- count(checklists_zf, month = month(observation_date))\n\n# plot monthly number of checklists\nggplot(monthly_checklists) +\n aes(x = month, y = n) +\n geom_line() +\n geom_point() +\n scale_x_continuous(breaks = 1:12) +\n scale_y_continuous(limits = c(0, NA), labels = scales::comma) +\n labs(x = \"Month of year\",\n y = \"# of eBird checklists\",\n title = \"Monthly eBird checklist submissions in Rio Grande do Sul\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n1.6.2 Converting to spatial format\nMany applications of eBird data require converting the data into an explicitly spatial format, for example, to make a map or subset the observations to only those within a polygon for a protected area. The R package sf can be use to represent spatial points, lines, and polygons in R. We can convert the data frame of checklists to an sf object using the latitude and longitude.\n\nchecklists_sf <- st_as_sf(checklists_zf, coords = c(\"longitude\", \"latitude\"),\n # 4326 is the code for an unprojected lon/lat\n # coordiante reference system\n crs = 4326)\nprint(checklists_sf)\n#> Simple feature collection with 16526 features and 9 fields\n#> Geometry type: POINT\n#> Dimension: XY\n#> Bounding box: xmin: -57.6 ymin: -35 xmax: -48.7 ymax: -27.1\n#> Geodetic CRS: WGS 84\n#> # A tibble: 16,526 × 10\n#> checklist_id observation_date time_observations_started protocol_type\n#> * <chr> <date> <chr> <chr> \n#> 1 S117444976 2013-05-18 07:47:00 Traveling \n#> 2 S117215031 2013-04-14 07:53:00 Traveling \n#> 3 S53740464 2013-01-11 08:00:00 Traveling \n#> 4 S16023053 2013-12-14 17:30:00 Stationary \n#> 5 S15844084 2013-12-03 13:00:00 Traveling \n#> 6 S117214399 2013-03-16 07:36:00 Traveling \n#> # ℹ 16,520 more rows\n#> # ℹ 6 more variables: duration_minutes <int>, effort_distance_km <dbl>,\n#> # number_observers <int>, observation_count <int>, species_observed <lgl>,\n#> # geometry <POINT [°]>\n\nImagine that we’re only interested in observations from within 50 km of Gramado, Brazil (29.375°S, 50.876°W). We can use sf to generate a circle of radium 50 km centered on Gramado, then subset observations to only those falling within the circle.\n\ngramado_point <- st_sfc(st_point(c(-50.876, -29.375)), crs = 4326)\n# 50km = 50,000m\ngramado_circle <- st_buffer(gramado_point, dist = 50000)\nchecklists_gramado <- checklists_sf[gramado_circle, ]\n\nWe could now, for example, calculate the detection frequency in the area around Gramado.\n\nmean(checklists_gramado$species_observed)\n#> [1] 0.097\n\nFinally, in many cases, it may be more convenient to work with eBird data outside of R in a GIS such as QGIS, a free and open source GIS. We can export the Fork-tailed Flycatcher observations to a GeoPackage using sf.\n\nwrite_sf(checklists_sf, \"data/fotfly-ebird_br-rs.gpkg\",\n layer = \"fotfly_observations\")\n\n\n\n\n\n\n\nTip\n\n\n\nThe GeoPackage is a modern, open source alternative to the shapefile format for storing spatial data. GeoPackages avoid many of the problems and limitations associated with shapefiles, and they are much more efficient than shapefiles. The online ArcGIS documentation provides instructions for how to open a GeoPackage in ArcGIS.\n\n\n\n\n1.6.3 Mapping\nNow that we have the data in a spatial format, we can produce a map of the Fork-tailed Flycatcher detection/non-detection data. For complex, highly customized maps, we recommend using a GIS such as QGIS that’s specifically designed for cartography. However, it is possible to make a quick, simple map in R.\nWe’ll start by loading polygons defining coutry and state borders, which will provide contextual information for our map. These polygons come from Natural Earth, and excellent source of global, attribution free spatial data. The R package rnaturalearth provides access to Natural Earth data within R; however, for convenience we’ve provided the necessary layers in the file data/gis-data.gpkg included in the data package for this workshop. We’ll also project everything to an equal area projection center on Rio Grande do Sul.\n\ncrs <- st_crs(\"+proj=laea +lat_0=-30 +lon_0=-50\")\n\n# polygons from natural earth\nne_land <- read_sf(\"data/gis-data.gpkg\", \"ne_land\") %>% \n st_transform(crs = crs)\nne_country_lines <- read_sf(\"data/gis-data.gpkg\", \"ne_country_lines\") %>% \n st_transform(crs = crs)\nrgds_boundary <- read_sf(\"data/gis-data.gpkg\", \"ne_states\") %>% \n filter(state_code == \"BR-RS\") %>% \n st_transform(crs = crs)\n\n# transform ebird data to equal area projection\nchecklists_proj <- st_transform(checklists_sf, crs = crs)\n\nNow we can make a map of observations of Fork-tailed Flycatcher in Rio Grande do Sul. We build up the map in layers, first creating a basemap with the Natural Earth polygons, then plotting the eBird detection and non-detection data on top. When building up a map in layes like this, it’s often useful to first plot a blank version the main dataset you intend to map to define the spatial extent of the map, then layer everything else on top, finishing with plotting the data a second time so it appears as the top layer.\n\npar(mar = c(0.25, 0.25, 2, 0.25))\n\n# start with a blank plot of the data to define the spatial extent of the map\nplot(st_geometry(checklists_proj), col = NA)\n\n# contextual gis data\nplot(ne_land, col = \"#dddddd\", border = \"#888888\", lwd = 0.5, add = TRUE)\nplot(rgds_boundary, col = \"#cccccc\", border = NA, add = TRUE)\n#> Warning in plot.sf(rgds_boundary, col = \"#cccccc\", border = NA, add = TRUE):\n#> ignoring all but the first attribute\nplot(ne_country_lines, col = \"#ffffff\", lwd = 1.5, add = TRUE)\n\n# ebird observations\n# not detected\nplot(filter(checklists_proj, !species_observed),\n pch = 19, cex = 0.2, col = alpha(\"#555555\", 0.5),\n add = TRUE)\n#> Warning in plot.sf(filter(checklists_proj, !species_observed), pch = 19, :\n#> ignoring all but the first attribute\n# detected\nplot(filter(checklists_proj, species_observed),\n pch = 19, cex = 0.3, col = alpha(\"#4daf4a\", 1),\n add = TRUE)\n#> Warning in plot.sf(filter(checklists_proj, species_observed), pch = 19, :\n#> ignoring all but the first attribute\n# legend\nlegend(\"bottomright\", bty = \"n\",\n col = c(\"#555555\", \"#4daf4a\"),\n legend = c(\"eBird checklists\", \"Fork-tailed Flycatcher sightings\"),\n pch = 19)\nbox()\ntitle(\"Fork-tailed Flycatcher eBird Observations (2013-2022)\")" }, { "objectID": "ebirdst.html#sec-ebirdst-access", diff --git a/docs/sitemap.xml b/docs/sitemap.xml index e9985dc..5da90c3 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -2,14 +2,14 @@ https://ebird.github.io/ebird-workshop-oca/index.html - 2023-07-19T19:20:48.584Z + 2023-07-20T16:57:42.021Z https://ebird.github.io/ebird-workshop-oca/ebird.html - 2023-07-19T19:20:48.616Z + 2023-07-20T21:23:18.040Z https://ebird.github.io/ebird-workshop-oca/ebirdst.html - 2023-07-19T19:20:48.665Z + 2023-07-20T16:55:39.478Z diff --git a/ebird.qmd b/ebird.qmd index 3787f81..9286186 100644 --- a/ebird.qmd +++ b/ebird.qmd @@ -36,7 +36,7 @@ If you would prefer to directly download the exact dataset used in this workshop The previous step left us with two tab separated text files, one for the EBD (i.e. observation data) and one for the SED (i.e. checklist data). Start a new RStudio project and put the downloaded text files in the `data/` sub-directory of the project directory. -The `auk` R package is specifically designed for working with eBird data. It contains the functions [`read_ebd()`](https://cornelllabofornithology.github.io/auk/reference/read_ebd.html) and [`read_sampling()`](https://cornelllabofornithology.github.io/auk/reference/read_ebd.html), designed to import the EBD and SED, respectively, into R. First let's import the checklist data (SED). +The `auk` R package is specifically designed for working with eBird data. It includes the functions [`read_ebd()`](https://cornelllabofornithology.github.io/auk/reference/read_ebd.html) and [`read_sampling()`](https://cornelllabofornithology.github.io/auk/reference/read_ebd.html) for importing the EBD and SED, respectively, into R. First let's import the checklist data (SED). ```{r} #| label: ebird-import-sed @@ -45,7 +45,6 @@ library(dplyr) library(ggplot2) library(lubridate) library(sf) -library(tmap) f_sed <- "data/ebd_BR-RS_fotfly_smp_relJun-2023_sampling.txt" checklists <- read_sampling(f_sed, unique = FALSE) @@ -58,7 +57,7 @@ glimpse(checklists) Take some time to explore the variables in the checklist dataset. If you're unsure about any of the variables, consult the metadata document that came with the data download (`eBird_Basic_Dataset_Metadata_v1.14.pdf`). ::: -Next we'll import the observation data. +Next, let's import the observation data. ```{r} #| label: ebird-import-ebd @@ -73,7 +72,7 @@ glimpse(observations) Take some time to explore the variables in the observation dataset. Notice that the EBD duplicates many of the checklist-level variables from the SED. ::: -When we read the data into R you probably noticed we used `unique = FALSE` and `rollup = FALSE`. By default the read functions in `auk` perform two important pre-processing steps: **combining duplicate shared checklists** and **taxonomic rollup**. We intentionally turned off this functionality for the purposes of demonstration. +When we read the data into R, we used `unique = FALSE` and `rollup = FALSE`. By default the read functions in `auk` perform two important pre-processing steps: **combining duplicate shared checklists** and **taxonomic rollup**. We intentionally turned off this functionality for the purposes of demonstration. ### Shared checklists {#sec-ebird-import-shared} @@ -104,9 +103,22 @@ head(checklists_unique$checklist_id) tail(checklists_unique$checklist_id) ``` +::: callout-tip +## Tip + +Curious when checklists and observers contributed to a shared checklist after it has been collapsed? The `sampling_event_identifier` and `observer_id` contain comma-separated lists of all checklists and observers that went into the shared checklists. + +```{r} +#| label: ebird-import-shared-tip +checklists_unique %>% + filter(checklist_id == "G10019405") %>% + select(checklist_id, group_identifier, sampling_event_identifier, observer_id) +``` +::: + ### Taxonomic rollup {#sec-ebird-import-rollup} -eBird observations can be made at levels below species (e.g. subspecies) or above species (e.g. a bird that was identified as a duck, but the species could not be determined); however, for most uses we'll want observations at the species level. This is especially true if we want to produce detection/non-detection data from complete checklists because "complete" only applies to species. +eBird observations can be made at levels below species (e.g. subspecies) or above species (e.g. a bird that was identified as a duck, but the species could not be determined); however, for most uses we'll want observations at the species level. This is especially true if we want to produce detection/non-detection data from complete checklists because "complete" only applies at the species level. ::: callout-tip ## Tip @@ -122,15 +134,23 @@ observations_rollup <- auk_rollup(observations) # only one checklist is affected by this observations %>% filter(sampling_event_identifier == "S99335111") %>% - select(sampling_event_identifier, common_name, subspecies_common_name) + select(sampling_event_identifier, common_name, subspecies_common_name, + observation_count) observations_rollup %>% filter(sampling_event_identifier == "S99335111") %>% - select(sampling_event_identifier, common_name) + select(sampling_event_identifier, common_name, + observation_count) ``` +::: callout-tip +## Tip + +If multiple taxa on a single checklist roll up to the same species, `auk_rollup()` attempts to combine them intelligently. If each observation has a count, those counts are added together, but if any of the observations is missing a count (i.e. the count is "X") the combined observation is also assigned an "X". +::: + ## Generating detection/non-detection data {#sec-ebird-zf} -Complete eBird checklists are extremely valuable because, for all species that weren't reported, we can infer counts of 0. This allows us to convert eBird from presence only data to detection/non-detection data, which allows for much more robust analyses. Note that we don't use the term presence/absence data here because a non-detection doesn't necessarily imply the species was absent, only that observer didn't detect and identify it. +Complete eBird checklists are extremely valuable because, for all species that weren't reported, we can infer counts of 0. This allows us to convert eBird from presence only data to detection/non-detection data, which allows for much more robust analyses. Note that we don't use the term presence/absence data here because a non-detection doesn't necessarily imply the species was absent, only that the observer didn't detect and identify it. We refer to the process of producing detection/non-detection data as "zero-filling" the eBird data because we're filling in the missing zeros. We'll read the eBird data into R again, filter to only complete checklists, then use the function [`auk_zerofill()`](https://cornelllabofornithology.github.io/auk/reference/auk_zerofill.html) to generate detection/non-detection data. Note that shared checklists are combined and taxonomic rollup is performed by default when using the `read_*()` functions from `auk`. @@ -170,14 +190,14 @@ select(zf, observation_count, species_observed) %>% ## Filtering data {#sec-ebird-filtering} -Now that you have a detection/non-detection dataset, it's likely that you want to do something with it. For example, you may want to make a map, use the eBird data to identify priority areas for a species, or train a species distribution model. Regardless of the specific application, it's likely that some amount of filtering of the data is required first. Some of the ways you may want to filter eBird data include: +Now that you have a detection/non-detection dataset, it's likely that you want to do something with it. For example, you may want to make a map, identify priority areas for a species, or train a species distribution model. Regardless of the specific application, it's likely that some amount of filtering of the data is required first. Some of the ways you may want to filter eBird data include: - **Temporal filtering**: filter the data to a specific range of years or to a specific time of year. - **Spatial filtering**: filter the data to focus on a specific region, e.g. a protected area. - **Increasing precision**: some eBird checklists are quite long in distance or duration leading to spatial or temporal imprecision. By removing longer checklists we can increase the spatial precision of the dataset. - **Reducing variation in effort**: unlike structured scientific surveys, data can be submitted to eBird using a variety of protocols and there is significant variation in effort between checklists in the eBird dataset. Variation in protocol and effort leads to variation in detectability (more effort generally leads to higher detectability). We can choose to impose more structure on the eBird dataset by filtering to reduce variation in protocol and effort. -The specific filtering you apply will depend on how you intend to use the eBird data. However, for the sake of this example, let's filter the eBird data to only traveling and stationary checklists from 2013-2022 that are less than 6 hours in duration and 10km in length. +The specific filtering you apply will depend on how you intend to use the eBird data. However, for the sake of this example, let's filter the eBird data to only traveling and stationary checklists from 2013-2022 that are less than 6 hours in duration and 10 km in length. ```{r} #| label: ebird-filtering-filter @@ -196,7 +216,7 @@ Finally, many of the columns in this data frame are unnecessary or redundant, so ```{r} #| label: ebird-filtering-select -checklists <- zf_filtered %>% +checklists_zf <- zf_filtered %>% select(checklist_id, latitude, longitude, observation_date, time_observations_started, @@ -213,14 +233,14 @@ The simplest thing we can do with these eBird observations is estimate the frequ ```{r} #| label: ebird-applications-freq-total -mean(checklists$species_observed) +mean(checklists_zf$species_observed) ``` -So, Fork-tailed Flycatcher is fairly common within this region with `r scales::percent(mean(checklists$species_observed))` of checklists detecting the species. Detection frequency can be used to compare the prevalence of a species between regions or over time. For example, Fork-tailed Flycatcher is migratory, so let's look at how detection frequency changes over the months of the year. +So, Fork-tailed Flycatcher is fairly common within this region with `r scales::percent(mean(checklists_zf$species_observed))` of checklists detecting the species. Detection frequency can be used to compare the prevalence of a species between regions or over time. For example, Fork-tailed Flycatcher is migratory, so let's look at how detection frequency changes over the months of the year. ```{r} #| label: ebird-applications-freq-monthly -monthly_detection <- checklists %>% +monthly_detection <- checklists_zf %>% mutate(month = month(observation_date)) %>% group_by(month) %>% summarize(detection_frequency = mean(species_observed)) @@ -253,7 +273,7 @@ There is significant variability in checklist submissions per month, with twice ```{r} #| label: ebird-applications-freq-sol -monthly_checklists <- count(checklists, month = month(observation_date)) +monthly_checklists <- count(checklists_zf, month = month(observation_date)) # plot monthly number of checklists ggplot(monthly_checklists) + @@ -274,7 +294,7 @@ Many applications of eBird data require converting the data into an explicitly s ```{r} #| label: ebird-applications-spatial-convert -checklists_sf <- st_as_sf(checklists, coords = c("longitude", "latitude"), +checklists_sf <- st_as_sf(checklists_zf, coords = c("longitude", "latitude"), # 4326 is the code for an unprojected lon/lat # coordiante reference system crs = 4326) @@ -310,12 +330,12 @@ write_sf(checklists_sf, "data/fotfly-ebird_br-rs.gpkg", ::: callout-tip ## Tip -[GeoPackage](https://www.geopackage.org/) is a modern, open source alternative to the shapefile format for storing spatial data. GeoPackages avoid many of the problems and limitations associated with shapefiles, and they are much more efficient than shapefiles. The online ArcGIS documentation provides [instructions for how to open a GeoPackage in ArcGIS](https://desktop.arcgis.com/en/arcmap/latest/manage-data/databases/connect-sqlite.htm). +The [GeoPackage](https://www.geopackage.org/) is a modern, open source alternative to the shapefile format for storing spatial data. GeoPackages avoid many of the problems and limitations associated with shapefiles, and they are much more efficient than shapefiles. The online ArcGIS documentation provides [instructions for how to open a GeoPackage in ArcGIS](https://desktop.arcgis.com/en/arcmap/latest/manage-data/databases/connect-sqlite.htm). ::: ### Mapping {#sec-ebird-applications-mapping} -Now that we have the data in a spatial format, we can produce a map of the Fork-tailed Flycatcher detection/non-detection data. For complex, highly customized maps, we recommend using a GIS such as QGIS that's specifically designed for cartography. However, it's possible to make a quick, simple map in R using the `tmap` package. +Now that we have the data in a spatial format, we can produce a map of the Fork-tailed Flycatcher detection/non-detection data. For complex, highly customized maps, we recommend using a GIS such as QGIS that's specifically designed for cartography. However, it is possible to make a quick, simple map in R. We'll start by loading polygons defining coutry and state borders, which will provide contextual information for our map. These polygons come from [Natural Earth](https://www.naturalearthdata.com/downloads/), and excellent source of global, attribution free spatial data. The R package `rnaturalearth` provides access to Natural Earth data within R; however, for convenience we've provided the necessary layers in the file `data/gis-data.gpkg` included in the [data package](index.qmd#sec-intro-data) for this workshop. We'll also project everything to an equal area projection center on Rio Grande do Sul. @@ -336,13 +356,13 @@ rgds_boundary <- read_sf("data/gis-data.gpkg", "ne_states") %>% checklists_proj <- st_transform(checklists_sf, crs = crs) ``` - +Now we can make a map of observations of Fork-tailed Flycatcher in Rio Grande do Sul. We build up the map in layers, first creating a basemap with the Natural Earth polygons, then plotting the eBird detection and non-detection data on top. When building up a map in layes like this, it's often useful to first plot a blank version the main dataset you intend to map to define the spatial extent of the map, then layer everything else on top, finishing with plotting the data a second time so it appears as the top layer. ```{r} #| label: ebird-applications-mapping-map -par(mar = c(0.25, 0.25, 0.25, 0.25)) +par(mar = c(0.25, 0.25, 2, 0.25)) -# set up plot area +# start with a blank plot of the data to define the spatial extent of the map plot(st_geometry(checklists_proj), col = NA) # contextual gis data @@ -365,6 +385,5 @@ legend("bottomright", bty = "n", legend = c("eBird checklists", "Fork-tailed Flycatcher sightings"), pch = 19) box() -par(new = TRUE, mar = c(0, 0, 3, 0)) -title("Fork-tailed Flycatcher eBird Observations\n2013-2022") +title("Fork-tailed Flycatcher eBird Observations (2013-2022)") ``` diff --git a/index.qmd b/index.qmd index b9f2086..30d9abc 100644 --- a/index.qmd +++ b/index.qmd @@ -8,18 +8,18 @@ editor_options: # Introduction {.unnumbered} -The contents of this website comprise the notes for a workshop on best practices for using [eBird](https://ebird.org/home) data and [eBird Status](https://science.ebird.org/en/status-and-trends) data products presented at the [Ornithological Congress of The Americas (OCA)](https://oca2023.com.br/evento/oca2023/home) in August 2023 in Gramado, Brazil. The workshop is divided into two lessons covering: +This website contains the notes for a set of two workshops on best practices for using [eBird](https://ebird.org/home) data and [eBird Status](https://science.ebird.org/en/status-and-trends) data products, respectively, presented at the [Ornithological Congress of The Americas (OCA)](https://oca2023.com.br/evento/oca2023/home) in August 2023 in Gramado, Brazil. The two workshops are: 1. [Best Practices for Using eBird Data](ebird.qmd): introduction to the eBird Basic Dataset (EBD), challenges associated with using eBird data for analysis, and best practices for preparing eBird data for modeling. 3. [eBird Status and Trends](ebirdst.qmd): downloading eBird Status data products, loading the data into R, and using them for a variety of applications. ## Setup {#sec-intro-setup} -This workshop is intended to be interactive. All examples are written in the R programming language, and the instructor will work through the examples in real time, while the attendees are encouraged following along by writing the same code. To ensure we can avoid any unnecessary delays, please follow these setup instructions prior to the workshop +This workshop is intended to be interactive. All examples are written in the R programming language, and the instructor will work through the examples in real time, while the attendees are encouraged following along by writing the same code. To ensure we can avoid any unnecessary delays, please follow these setup instructions prior to the workshop: 1. [Create an eBird account](https://ebird.github.io/ebird-best-practices/intro.html) if you don't already have one and request access to the raw eBird data and/or the eBird Status data products depending on which workshops you're attending: -- Best Practices for Using eBird Data: [request access to the eBird Basic Dataset](https://ebird.org/data/download). -- eBird Status and Trends: [request access to the eBird Status data products](https://science.ebird.org/en/status-and-trends/download-data) + - Best Practices for Using eBird Data: [request access to the eBird Basic Dataset](https://ebird.org/data/download). + - eBird Status and Trends: [request access to the eBird Status data products](https://science.ebird.org/en/status-and-trends/download-data) 2. [Download](https://cloud.r-project.org/) and install the latest version of R. **You must have R version 4.0.0 or newer to follow along with this workshop** 3. [Download](https://posit.co/download/rstudio-desktop/#download) and install the latest version of RStudio. RStudio is not required for this workshop; however, the instructors will be using it and you may find it easier to following along if you're working in the same environment. 4. The lessons in this workshop use a variety of R packages. To install all the necessary packages, run the following code @@ -34,8 +34,8 @@ remotes::install_github("ebird/ebird-best-practices") ``` 5. Ensure all packages are updated to their most recent versions by clicking on the Update button on the Packages tab in RStudio. 6. Download the data package for the workshop you are attending: -- [Best Practices for Using eBird Data](https://drive.google.com/file/d/1y7MXbiqGzwJpyIsDDwA8ErqNVueGgecF/view?usp=sharing) -- [eBird Status and Trends](https://drive.google.com/file/d/1mXMO2mxqERYkXcneITmuZpmA4Jfj53p3/view?usp=sharing) + - [Best Practices for Using eBird Data](https://drive.google.com/file/d/1y7MXbiqGzwJpyIsDDwA8ErqNVueGgecF/view?usp=sharing) + - [eBird Status and Trends](https://drive.google.com/file/d/1mXMO2mxqERYkXcneITmuZpmA4Jfj53p3/view?usp=sharing) ## Tidyverse {#sec-intro-tidyverse}