My contributions to the Great Southern Bioblitz in Cairns

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_cairns = projects['results'][0]

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

Project _Great Southern BioBlitz 2025: Cairns_ has 571 observations in iNaturalist

records=list()
j=1
while len(records) < n_obs:
    print("Requesting observations from project _{}_: page {}, total of {} observations downloaded".format(gbs_cairns['title'],j,min(j*200,n_obs)))
    observations = get_observations(project_id=gbs_cairns['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: Cairns_: page 1, total of 200 observations downloaded
Requesting observations from project _Great Southern BioBlitz 2025: Cairns_: page 2, total of 400 observations downloaded
Requesting observations from project _Great Southern BioBlitz 2025: Cairns_: page 3, total of 571 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 0x12cb26de0>

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	60	51	38	10
Amphibia	6	6	4	5
Animalia	28	26	18	10
Arachnida	47	46	36	14
Aves	190	185	90	31
Fungi	7	6	6	6
Insecta	104	88	77	28
Mammalia	2	2	2	2
Mollusca	16	13	12	5
Plantae	90	82	67	20
Protozoa	1	0	0	1
Reptilia	20	18	12	10

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']>10]

		uuid	species_guess
		count	count	nunique
user_id	user_name
74355	JR Ferrer-Paris	93	83	62
1147408	Andy Tuckey	22	22	22
1453488	Shannon Tushingham	26	26	22
1771883	Graham Winterflood	11	11	9
4957699	Sacha	33	33	33
5414239	SkippingShoe	12	12	12
6287892	Dr Spoodle Doodle	14	14	10
6364733	jimchurches	40	40	33
8277420	Matt Boyle	16	16	15
9125842	JimsWildLife	56	50	35