1. Not the most notable, but possibly my favourite 2022 sighting: a dancing caterpillar!

    Sadly, this isn’t an extremely rare musical species which will revolutionise animal rhythmic entrainment research! It’s a defensive behaviour triggered in this case by the fly which I was actually trying to photograph.

  2. 2022 Day 4 Highlights — was pretty tired at this point so just went for a short walk through the Laaer Wald. Not much of note, but I did finally get to see a bristly millipede

    Also found a buzzing spider and a weevil with an amusingly long nose

    That’s a total of 260 observations over the four days, of which 106 have already been IDed to species. I’ll wait a few days before posting my usual unique and notable species analysis, and will update it a few times as more observations get IDs.

  3. I’ve wanted to see an Alpine Rosalia ever since I first heard about them, and finally managed to spot two today! Very impressive beetles, lots of fun to watch them move, and they make a cute little scratching sound when disturbed.

  4. Thinking a pigeon is a bird of prey, because it’s perched in a tree, is easily my funniest iNaturalist computer vision fail yet.

  5. Some more hamster activity today! Anyone have any idea what the behaviour at 0:57 is? Looks like it’s trying to flatten grass, but I imagine it’d rather rest in its hole than on the surface.

    Update: based on discussion in this thread, looks like it’s probably scent-marking after a territorial dispute with another male.

  6. Ridiculous amounts of hamster activity at the moment. I must have seen nearly 100 of them in an hour. I also heard them vocalise for the first time, a sort of guttural hissing sound.

  7. Had some fun looking at soil with a cheap USB microscope today. At maximum magnification, its depth of field is smaller than the height of one of these tiny soil mites (maybe 0.3mm at the most)

  8. Here’s a python snippet for analysing an iNaturalist export file and exporting an HTML-formatted list of species which only have observations from a single person (e.g. this list for the CNC Wien 2021)

    # coding: utf-8
    
    import argparse
    import pandas as pd
    
    """
    Find which species in an iNaturalist export only have observations from a single observer.
    
    Get an export from here: https://www.inaturalist.org/observations/export with a query such
    as quality_grade=research&identifications=any&rank=species&projects[]=92926 and at least the
    following columns: taxon_id, scientific_name, common_name, user_login
    
    Download it, extract the CSV, then run this script with the file name as its argument. It will
    output basic stats formatted as HTML.
    
    The only external module required is pandas.
    
    Example usage:
    
    		py uniquely_observed_species.py wien_cnc_2021.csv > wien_cnc_2021_results.html
    
    If you provide the --project-id (-p) argument, the taxa links in the output list will link to 
    a list of observations of that taxa within that project. Otherwise, they default to linking
    to the taxa page.
    
    If a quality_grade column is included, non-research-grade observations will be included in the
    analysis. Uniquely observed species with no research-grade observations will be marked. Species
    which were observed by multiple people, only one of which has research-grade observation(s) will
    also be marked.
    
    By Barnaby Walters waterpigs.co.uk
    """
    
    if __name__ == "__main__":
    	parser = argparse.ArgumentParser(description='Given an iNaturalist observation export, find species which were only observed by a single person.')
    	parser.add_argument('export_file')
    	parser.add_argument('-p', '--project-id', dest='project_id', default=None)
    
    	args = parser.parse_args()
    
    	uniquely_observed_species = {}
    
    	df = pd.read_csv(args.export_file)
    
    	# If quality_grade isn’t given, assume that the export contains only RG observations.
    	if 'quality_grade' not in df.columns:
    		df.loc[:, 'quality_grade'] = 'research'
    
    	# Filter out casual observations.
    	df = df.query('quality_grade != "casual"')
    
    	# Create a local species reference from the dataframe.
    	species = df.loc[:, ('taxon_id', 'scientific_name', 'common_name')].drop_duplicates()
    	species = species.set_index(species.loc[:, 'taxon_id'])
    	
    	for tid in species.index:
    		observers = df.query('taxon_id == @tid').loc[:, 'user_login'].drop_duplicates()
    		research_grade_observers = df.query('taxon_id == @tid and quality_grade == "research"').loc[:, 'user_login'].drop_duplicates()
    
    		if observers.shape[0] == 1:
    			# Only one person made any observations of this species.
    			observer = observers.squeeze()
    			if observer not in uniquely_observed_species:
    				uniquely_observed_species[observer] = []
    
    			uniquely_observed_species[observer].append({
    				'id': tid,
    				'has_research_grade': (not research_grade_observers.empty),
    				'num_other_observers': 0
    			})
    		elif research_grade_observers.shape[0] == 1:
    			# Multiple people observed the species, but only one person has research-grade observation(s).
    			rg_observer = research_grade_observers.squeeze()
    			if rg_observer not in uniquely_observed_species:
    				uniquely_observed_species[rg_observer] = []
    			
    			uniquely_observed_species[rg_observer].append({
    				'id': tid,
    				'has_research_grade': True,
    				'num_other_observers': observers.shape[0] - 1
    			})
    	
    	# Sort observers by number of unique species.
    	sorted_observations = sorted(uniquely_observed_species.items(), key=lambda t: len(t[1]), reverse=True)
    
    	print(f"<p>{sum([len(t) for _, t in sorted_observations])} taxa uniquely observed by {len(sorted_observations)} observers.</p>")
    
    	print('<p>')
    	for observer, _ in sorted_observations:
    		print(f"@{observer} ", end='')
    	print('</p>')
    
    	print('<p><b>bold</b> species are ones for which the given observer has one or more research-grade observations.</p>')
    	print('<p>If only one person has RG observations of a species, but other people have observations which need ID, the number of needs-ID observers are indicated in parentheses.')
    
    	for observer, taxa in sorted_observations:
    		print(f"""\n\n<p><a href="https://www.inaturalist.org/people/{observer}">@{observer}</a> ({len(taxa)} taxa):</p><ul>""")
    		for tobv in sorted(taxa, key=lambda t: species.loc[t['id']]['scientific_name']):
    			tid = tobv['id']
    			t = species.loc[tid]
    
    			if args.project_id:
    				taxa_url = f"https://www.inaturalist.org/observations?taxon_id={tid}&amp;project_id={args.project_id}"
    			else:
    				taxa_url = f'https://www.inaturalist.org/taxa/{tid}'
    			
    			rgb, rge = ('<b>', '</b>') if tobv.get('has_research_grade') else ('', '')
    			others = f" ({tobv.get('num_other_observers', 0)})" if tobv.get('num_other_observers', 0) > 0 else ''
    
    			if not pd.isnull(t['common_name']):
    				print(f"""<li><a href="{taxa_url}">{rgb}<i>{t['scientific_name']}</i> ({t['common_name']}){rge}{others}</a></li>""")
    			else:
    				print(f"""<li><a href="{taxa_url}">{rgb}<i>{t['scientific_name']}</i>{rge}{others}</a></li>""")
    		print("</ul>")
    
  9. Day 4 of the was very successful after the storm-related inactivity of day 3, with 106 observations from the Lobau and Prater. Highlights include:

    My first sighting of wild european pond turtles

    My first black woodpeckers

    Many great spotted woodpeckers

    A greater bee fly which held still long enough for me to photograph it

    Some newts

    This interesting spider

    These enormous beetle larvae

    This cool looking moth

    And many, many oil beetles

    That makes a total of 213 observations over the long weekend. It’ll take some time for them to be identifed down to species level, but it looks like at least 130 individual species.

  10. Day 2 of the : I finally made it to the Lainzer Tiergarten and made 73 observations, the highlights of which included:

    Some enormous woodlice

    Some sort of Polydesmus, curled up on a little wall it had built to protect its eggs

    Plenty of Glomeris and pill woodlice

    A red squirrel

    A nuthatch — not a great photo, but they’re one of my favourite woodland birds

    One of the furriest moths I’ve ever seen — I think it’s a chimney sweep moth? If so, it’s an appropriate name.

    And finally, this enormous severed stag beetle head.