My contributions to the Great Southern Bioblitz in the Great Barrier Reef

A summary of my iNaturalist observations during our visit to Cairns.

Load Python modules

import pandas as pd
import folium
from datetime import datetime, timedelta
from pyinaturalist import (
    Observation,
    get_observations,
    get_projects_by_id,
    pprint,
)
import ipyplot
from itertools import compress

import seaborn as sns
import matplotlib.pyplot as plt

Download iNaturalist observations

From the end of May to the first week of June 2025

projects = get_projects_by_id([253831, 255314])
pprint(projects)

                                                                                                                   
  ID       Title                                         Type         URL                                          
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 
  255314   Great Southern BioBlitz 2025: Cairns          collection   https://www.inaturalist.org/projects/255314  
  253831   Great Southern Bioblitz 2025: Great Barrier   collection   https://www.inaturalist.org/projects/253831  
           Reef                                                                                                    
                                                                                                                   

gbs_gbr = projects['results'][1]

observations = get_observations(project_id=gbs_gbr['id'], 
                                per_page=0)
n_obs = observations['total_results']
print("Project _{}_ has {} observations in iNaturalist".format(gbs_gbr['title'],n_obs))

Project _Great Southern Bioblitz 2025: Great Barrier Reef_ has 1722 observations in iNaturalist

records=list()
j=1
while len(records) < n_obs:
    print("Requesting observations from project _{}_: page {}, total of {} observations downloaded".format(gbs_gbr['title'],j,min(j*200,n_obs)))
    observations = get_observations(project_id=gbs_gbr['id'],per_page=200,page=j)
    for obs in observations['results']:
        record = {
            'uuid': obs['uuid'], 
            'user_id': obs['user']['id'],
            'user': obs['user']['login'],
            'user_name': obs['user']['name'],
            'user_icon': obs['user']['icon_url'],
            'day_observed': obs['observed_on_details']['day'],
            'species_guess': obs['species_guess'],
            'quality_grade': obs['quality_grade'],
            'description': obs['description'],
            'location': obs['location']
            }
        if obs['taxon'] is not None:
            if 'iconic_taxon_name' in obs['taxon'].keys():
                record['iconic_taxon'] = obs['taxon']['iconic_taxon_name']
        if len(obs['observation_photos'])>0:
            record['url'] = obs['observation_photos'][0]['photo']['url']
            record['attribution'] = obs['observation_photos'][0]['photo']['attribution']
        if obs['oauth_application_id'] is None:
            record['app'] = 0
        else:
            record['app'] = obs['oauth_application_id']
            
        records.append(record)
    j=j+1

Requesting observations from project _Great Southern Bioblitz 2025: Great Barrier Reef_: page 1, total of 200 observations downloaded
Requesting observations from project _Great Southern Bioblitz 2025: Great Barrier Reef_: page 2, total of 400 observations downloaded
Requesting observations from project _Great Southern Bioblitz 2025: Great Barrier Reef_: page 3, total of 600 observations downloaded
Requesting observations from project _Great Southern Bioblitz 2025: Great Barrier Reef_: page 4, total of 800 observations downloaded
Requesting observations from project _Great Southern Bioblitz 2025: Great Barrier Reef_: page 5, total of 1000 observations downloaded
Requesting observations from project _Great Southern Bioblitz 2025: Great Barrier Reef_: page 6, total of 1200 observations downloaded
Requesting observations from project _Great Southern Bioblitz 2025: Great Barrier Reef_: page 7, total of 1400 observations downloaded
Requesting observations from project _Great Southern Bioblitz 2025: Great Barrier Reef_: page 8, total of 1600 observations downloaded
Requesting observations from project _Great Southern Bioblitz 2025: Great Barrier Reef_: page 9, total of 1722 observations downloaded

project_obs = pd.DataFrame(records)

Map of observations

map = folium.Map(tiles="Esri NatGeoWorldMap")

fg = folium.FeatureGroup(name="iNaturalist observations", control=True, attribution="observers @ iNaturalist").add_to(map)
popup_text = """<img src='{url}'>
<caption><i>{species}</i> observed on day {observed_on} / {attribution}</caption> {desc}
   """
popup_text_no_foto = """
<caption><i>{species}</i> observed on day {observed_on} / No foto</caption> {desc}
   """
for obs in records:
    if obs['quality_grade'] == 'research':
        if obs['description'] is None:
            desc = ""
        else:
            desc = obs['description']
        pincolor = 'green'
    else:
        desc = "Observation is not research quality grade."
        pincolor = 'gray'
    if 'url' not in obs.keys():
        fg.add_child(
            folium.Marker(
                location=obs['location'],
                popup=popup_text_no_foto.format(
                   species=obs['species_guess'],
                    observed_on=obs['day_observed'],
                    desc=desc),
                icon=folium.Icon(color=pincolor),
            )
          )
    else:
        fg.add_child(
            folium.Marker(
                location=obs['location'],
                popup=popup_text.format(
                   species=obs['species_guess'],
                    observed_on=obs['day_observed'],
                    desc=desc,
                   url = obs['url'],
                   attribution = obs['attribution']),
                icon=folium.Icon(color=pincolor),
            )
          )

folium.LayerControl().add_to(map)

<folium.map.LayerControl object at 0x12f688800>

map.fit_bounds(map.get_bounds())
map

Make this Notebook Trusted to load map: File -> Trust Notebook

Observations per iconic taxon

project_obs.groupby(['iconic_taxon']).agg({
    'uuid': 'count',
    'species_guess': ['count',pd.Series.nunique],
    'user_id': pd.Series.nunique})

	uuid	species_guess		user_id
	count	count	nunique	nunique
iconic_taxon
Actinopterygii	484	456	256	25
Animalia	268	252	129	33
Arachnida	12	11	8	4
Aves	123	120	52	20
Chromista	12	12	6	2
Fungi	3	1	1	3
Insecta	23	20	16	13
Mammalia	5	5	5	5
Mollusca	228	211	70	28
Plantae	97	82	65	17
Reptilia	26	24	9	15

data_crosstab = pd.crosstab(project_obs['iconic_taxon'],
                            project_obs['day_observed'], 
                               margins = False)

plt.figure(figsize=(8, 6))
sns.heatmap(data_crosstab, annot=True, fmt='d', cmap='YlGnBu', linewidths=.5)
plt.title('Heatmap of observations per day of Bioblitzing')
plt.ylabel('Iconic taxon')
plt.xlabel('Day of observation')
plt.tight_layout()
plt.show()

Top observers

top_obs = project_obs.groupby(['user_id','user_name']).agg({
    'uuid': 'count',
    'species_guess': ['count',pd.Series.nunique]})

top_obs.loc[top_obs['uuid']['count']>50]

		uuid	species_guess
		count	count	nunique
user_id	user_name
74355	JR Ferrer-Paris	87	77	57
4677927	Adam Smith	316	316	227
8277420	Matt Boyle	125	125	113
9125842	JimsWildLife	53	47	33
9717763	Rachel Bowater	53	49	45
9806282	Camille Workman	58	52	47
9948261	Luke Farrell	457	0	0