Naturalist Participation Varies Between Arizona Counties

iNaturalist participation is unevenly distributed at the global, national, and local scale. I looked at county-level observations in Arizona to try to identify trends and potential causes of variability in total observations and percent of observations identified.

Introduction

Simple hypotheses for the total number of observations in each county could be that the iNaturalist observations vary proportionally to population (based on number of observers) or based on land area (based on availability of organisms to observe).  Alternately, a hypothesis could be that areas with more species, or areas with more parks and tourist attractions, might attract more attention from iNaturalist users. 

Simple hypotheses for the percent of observations identified could be that this varies with the population of a county, so that larger population counties would be more likely to have more experts able to identify local observations.  Although counties with more people might also have more total observations, so this hypothesis could suggest that the percent of observations identified is either consistent across counties or, if the identifier effect predominates, there could be a greater percent in counties with more people.  Conversely, rural counties with low populations that also have lots of observations from visitors (such as to national parks) might have a lower proportion of observations identified.

I queried the iNaturalist.org website in December, 2025 for total observations and Research Grade (RG) for each Arizona county.  Some taxa, like fish or reptiles, might me more abundant in one part of the state over another, so to ensure consistency, I chose a taxa of species that is relatively evenly distributed across Arizona:  flowering plants (angiosperms).  I also obtained county population and land area from publicly available information on the web.  I then calculated % observations ID’d to RG, total species observed, and total RG species confirmed for each county and averages across all counties.

Results

Arizona has 15 counties that vary by orders of magnitude in size, population, as well as other factors that contribute to iNaturalist participation such as number of national parks and tourists.  The largest county by population is Maricopa county, with more than 4.5 million people, whereas the smallest county has less than 10,000 people.  The largest county by land area is Coconino county, with more than 18,000 square miles, whereas Santa Cruz, the smallest county, only has a little more than 1,000 square miles. 

Figure 1 - Arizona Counties

iNaturalist observations of flowering plants vary from only about 3,000 in Greenlee county, to almost 300,000 in Pima county.  RG observations vary from less than 2,000 in Greenlee county, to more than 200,000 in Pima county, with corresponding proportions of observations identified ranging from a low of 46% in Navajo county to a high of 74% in La Paz county. 

Counties vary in documented biodiversity from a high of almost 1,900 species in Pima county, to a low of about 430 species in La Paz county. 

AZ Counties	angiosperm observations	RG angiosperm	% ID'd	species count	RG species	% RG species
Pima	298,000	211,000	71%	2,396	1,855	77%
Coconino	134,440	61,500	46%	2,197	1,489	68%
Cochise	122,500	79,550	65%	1,860	1,460	78%
Yavapai	87,000	48,620	56%	1,798	1,326	74%
Santa Cruz	47,000	28,000	60%	1,586	1,273	80%
Maricopa	217,000	156,000	72%	1,800	1,200	67%
Pinal	41,000	29,000	71%	1,288	976	76%
Gila	26,233	15,500	59%	1,240	938	76%
Mohave	36,000	26,000	72%	1,200	890	74%
Apache	20,468	9,575	47%	1,182	832	70%
Graham	12,700	7,780	61%	1,044	774	74%
Navajo	14,000	6,400	46%	1,000	714	71%
Yuma	18,800	13,800	73%	682	521	76%
Greenlee	3,300	1,895	57%	617	432	70%
La Paz	8,110	6,000	74%	517	429	83%
Average	72,437	46,708	62%	1,360	1,007	74%
Total	1,086,551	700,620		20,407	15,109

Table 1 Arizona Counties Angiosperm plant observations and % Identified

To investigate trends across counties I used Tableau Public to create graphs of total observations versus county land area and population.  Because Maricopa county has 3.5 million more people than the next largest county, I excluded it from the analysis as an outlier. 

While there is a trend toward more observations in larger geographic areas, it is not very strong; the county with the greatest number of observations is right in the middle of the distribution of sizes.  The trend toward more observations with more people is a bit better, but it is dominated by the 2^nd most populated county in Arizona, Pima county.  Removing that county from the analysis essentially eliminates the trend, as the next largest county by population (Pinal) is below the average.

 

Figure 2 Observations by Land Area and Population, excluding Maricopa county.

I then looked at whether the activity of identifies varies with land area or population, and again I did not find simple trends that explain the variation.  It does appear that counties with smaller areas are more likely to have a greater proportion of reported species and observations identified.  This could be explained by the greater tractability of small areas, where large counties have more possible habitats, more potential species, and thus pose greater difficulty to botanists to identify plants across the whole county.  While the largest county, Coconino, does have the largest number of species reported, the smallest county (Santa Cruz) has more than the average number of species reported.  Also, two counties that are in the middle of the distribution (Yavapai and Cochise), have some of the highest number of reported species. 

Figure 3 RG Observations %R by Land Area and Population, excluding Maricopa county.

The trend between population and proportion of identified species is even weaker, with almost no trend apparent.  This leads me to reject the hypothesis that more people in an area necessarily lead to more identifications.

Instead, my current hypothesis, is that observation count is driven by other factors outside of total population or land area.  For example, I think that tourists who visit natural wonders such as the Grand Canyon disproportionately increase the number of observations and species in Coconino county, which results in a corresponding decrease in the proportion of observations and species identified.  In contrast, a county that has a strong resident population of people interested in nature, such as Pima county (Tucson), has a large  number of observations and species, but also has  a large proportion of those observations identified to species.

 

 

 

 

Atmospheric Streams Subsidize Valley Forests

I invented a new term to describe small-scale flows of water in the atmosphere.  Just as atmospheric rivers are large flows that transport tropical moisture thousands of miles to the mid-Latitudes, atmospheric streams share the moisture of the mountains with the valleys.

Example of an atmospheric river: Hurricane Priscilla projected track from October 7, 2025.  The remains of this storm brought copious moisture to the desert Southwest.

I first starting thinking about this when I noticed that the new weather station in the Watson Woods Riparian Preserve was often colder in the mornings than weather stations on the surrounding hills.  

Note the 40 degree temperature swing from cool (30's!) temperatures at night, to warm (80's) temperature during the day.

This is caused by katabatic winds from the mountains:

"On clear nights with calm winds, the ground cools rapidly. Air in contact with the colder ground cools by conducting heat to the ground. When this cooling process occurs along mountain slopes, the cooling air becomes colder and denser than the air away from the slopes, which causes the cold air to sink downslope. The dense cold air flows downslope in streams (called katabatic winds) following the steepest slopes. When the cold air flows into a relatively flat area (a mountain or river valley, for example), the streams of cold air slow down. This causes the valley to fill with cold air, much like streams filling a lake. "(MountWashington.org)

Hubbard Brook Experimental Forest, a good example of cold air drainage.

Atmospheric streams are distinct from the riparian drainages they follow, because air flows differently than water:

"Air flows in much larger volumes relative to the topographic surface. Water, even in hillside gullies, flows in volumes that are small relative to the scale of the landscape, and hence topography is the major control on the flow. Air masses are generally much larger relative to the landscape. This can lead to rather different effects. When a shallow cold air flow is moving slowly or is strongly stratified, it can become trapped by topographic barriers that would not trap water. Conversely, when the cold air flow is rapid or has lower stratification, it can flow over barriers, rather than go around them and so minimize friction.” (Research Meteorology)

Cold air flows are an important part of riparian ecology.  A study at the Coweeta Long Term Ecological Research (LTER) site found that cold air drainage subsidizes valley ecosystem productivity.  The study observed lower temperature air from the mountains cooling riparian forests, which lowered their carbon loss due to plant respiration.  The cool air must be a welcome respite for plants during the heat of summer.

Image from Coweeta LTER site in the South Carolina Appalachian mountains.

Cool mountain air can also be moister than valley air, especially in arid regions like Arizona.  Riparian streams carry water from mountains to valleys, while invisible atmospheric streams carry water in the form of humidity.  The extra boost in humidity only becomes visible (as fog) when the temperature drops below the dew point. The studies I looked at did not measure humidity, but it makes sense that higher elevation forests would have moister air than the hotter valleys.  When they share their air, they share their water.

Atmospheric streams are an important, but often overlooked, part of the global water cycle that carries moisture from the land to the ocean.  The recycling and transport of water from one part of the land to another part is sometimes called the "small water cycle".  We still have much to learn about the way our planet works!

El autobus magico: viaja por el agua

Joshua Trees Flower Episodically in Arizona

Intro

Joshua trees are large, visually-striking trees in the Mojave desert.  Most of them are in California and Nevada, but there is also a population in the western part of Arizona.  They are frequently photographed and there are more than 17,000 observations (1,000 in AZ) saved on iNaturalist, a website and app used to document biodiversity.  

Arizona Joshua Tree locations observed on iNaturalistiNaturalist.

Joshua trees produce showy clusters of white flowers at the tips of their branches in March, but this year I noticed they were not flowering.  I wondered how often they produce flowers and decided to answer this question using data from iNat.

53 observations in Arizona showed evidence of flowering, always in March and April.  

Methods

iNat observations of Joshua Trees (Yucca brevifolia) in Arizona were marked using the Plant Phenology option in the Annotation Field.  Plant phenology (flower budding, flowering, or fruiting) was determined based on visual inspection of the saved photos in iNat.

The iNat website has a nice phenology visualization tool.  This view is filtered to show only Arizona observations.  However, it cannot show differences in phenology from one year to the next.  

Observations were then filtered on the iNat Explore page by adding &term_id=12&term_value_id=13 to the URL and downloaded for analysis in Excel.

More info about using iNat search URLs.

Results

I graphed the results starting in 2017, because there are fewer iNat observations before 2017.  It appears Joshua Trees flower episodically.  According to this data, Joshua Trees flowered in four out of the last 7 years.  They appear to alternate years from 2017-2020, but then skipped 2021 and flowered in both 2022 and 2023.  

It is interesting to note that the Arizona Joshua Trees didn't flower in 2020, which was one of the wettest springs in the last 10 years. I wonder whether the spring moisture determines flowering, or if perhaps other climate variables, such as moisture in the fall, are more important.  Another possibility could be that the trees are only able to flower every other year, and that the trees were inhibited from flowering in 2020 because of the large number that flowered in 2019.  

According to this data they were able to flower in both 2022 and 2023, but at reduced numbers both years.  Perhaps the plants flowering in 2023 were different from the trees that had already flowered in 2022?  I can't answer that question with this iNat data.  While it appears that the areas of flowering in 2022 and 2023 were the same, not all of the Joshua trees in a given area necessarily flower, even in good years...

More Springtime NDVI Observations

 I recently took a driving tour from Prescott to Cottonwood and back.  Most areas have dried out already.  This post reviews the phenology, NDVI greenness maps, and rainfall patterns in the area.

Verde Valley  - wildflowers, hedgehog cactus flowers

2,437 32 AGDD 

501 50 AGDD

I-17 and General Crook  - mesquite just starting to flower, grasses dry

2,202 32 AGDD 

394  50 AGDD

Verde Valley native desert grassland with scattered juniper.  Some wildflowers are present, but most areas are dry.

Dugas - mesquite just starting to flower, grasses dry

2,414 

462 50 AGDD

169 and I-17 - Mesquite still leafing out, some grasses still green

2,224  32 AGDD 

400 50 AGDD

Hills near Dugas with dry invasive annual grasses.  Few to no flowers present.

89A at base of Mingus mountain grasses greening up, still early in the growing season

1,773  32 AGDD 

89A at base of Mingus mountain powerline ROW showing early spring green up of cool season grasses.

NDVI

DroughtView is still showing anomalously green areas between Cordes and Flower Pot along I-17, but these hills around Dugas are already quite dry (see image above).

Most recent (4/6-4/21) NDVI difference map

It matches the NDVI variance from that time period.  At this time there was still anomalous green up around Dugas, for example.  

MODIS NDVI (Near Real Time 8 Day) 4/6-4/21

DroughtView also shows straight NDVI, with a nice mask that only shows areas that are green, and has much more recent data.  For example, the current Near Real time NDVI shows greenness only in the mountains and areas of mesquite that have greened up, shows brown over much of the grasslands.  This is what I observed driving through the area: it has already browned out.  This tool allows visualize of current state of greenness on the landscape.

The difference a few weeks can make for springtime greenness.  Note the difference along I-17 from Cordes to Flower Pot.

MODIS NDVI (Near Real Time 8 Day) 4/22-5/7

QuickDRI (updated 5/6) is still showing "improvement" in drought stress around Congress and South of Cordes lakes, but NDVI (above) shows no greenness there.  I think it could be mesquite leaf out, but interesting that NDVI NRT doesn't show it.  Both maps continue to agree that the area around Yava still looks good.  QuickDRI shows "stress intensification" in the Bradshaw and Mingus  mountain areas that NDVI NRT shows as green.  Both could be true.  QuickDRI shows "improvement" in Chino Valley, an area that NDVI NRT doesn't show any greenness.  This area is still somewhat greening up, so QuickDRI may be more accurate there.

QuickDRI (updated 5/6)

Phenology and Accumulated Precipitation

Most areas are below normal 90 day precipitation, except a small area around Perkinsville.  This area doesn't show up as being anomalously green, or having any greenness in NDVI NRT, or having lower stress in QuickDRI.

Feb-May rainfall

Based on AGDD, it is possible that this area is still too early in Spring and needs more time to green up.  

The area around Congress has received some moisture in the last 60 days, but it may not be enough to compensate for the low rainfall in February, or the high temperatures and complete lack of precipitation over the last 30 days.  

March-May rainfall

April rainfall