Nutritional Yeast contains variable amounts of B vitamins

Nutritional yeast is tasty and nutritious!  How nutritious is it?  Well, that depends on whether it is fortified or not, and how much information you find on the nutrition label.  

After looking at the labels of about a dozen brands, I found that nutritional yeast is consistently a good source of B2, B3, and B5, and with fortification is a great source of all major B vitamins except probably B7.

Fortified nutritional yeast contains extra B vitamins and sometimes iron. These extra nutrients are added during the processing of the product to make it more nutritional.  The vitamin content of fortified nutritional yeast depends on how much it is fortified.

For example, here are two popular brands, Braggs and Bob's Red Mill:

% Daily Value (DV) per 2 tablespoon serving. Data reported from nutritional labels.

Both brands add supplement B1, B2, B3, B6, B9, and B12, but they add different amounts.  In general, both seem to try to add more than 200% and less than 500% of the daily value.  Bragg adds more of most B vitamins, but Bob's Red Mill adds more Folate (B9).  Neither brand reports any B5 or B7, but that doesn't mean they don't have any.  According to my analysis of unfortified nutritional yeast, they probably just didn't bother to measure and/or report it.

Non Fortified or Unfortified Nutritional Yeast contains only the vitamins and minerals found in the yeast cells and has no added vitamins and minerals.  It contains variable amounts of B vitamins due to differences in yeast strain, growth substrate, growing conditions, and deactivation temperature.  

While some brands (like Anthony's) don't report any nutritional values, I was able to find 7 brands on Amazon that reported nutritional testing results for their products:

% Daily Value (DV) per 2 tablespoon serving, data reported from nutritional labels.

Note that unfortified nutritional yeast does not contain B12.  For other B vitamins, the percent daily values are generally less than 100% and usually less than 50% per 2 Tablespoon serving.  

Another way to visualize this information is by Brand:

% Daily Value (DV) per 2 tablespoon serving, data reported from nutritional labels.

Microingredients and Naturebell are the most consistent.  Sari reports the least number of vitamins.  Food Alive reports some very high levels but also some very low levels.  Revly is also missing several vitamins.  They also reported more than 400% B2 but I removed this outlier; it may have been due to testing fortified nutritional yeast by mistake.

The differences between brands are larger than the similarities.  Some brands reported zero or almost zero B1, B2, B6, and B7.  Conversely, other brands reported over 100% DVs of B1, B2, B5, and B6.  Only B3 and B5 were consistently reported with decent amounts across all brands.  

If we assume unreported values are not zero but were just not measured and look only at the vitamins reported by each brand, the mean vitamin content of unfortified nutritional yeast looks pretty good.  However, the standard deviations are as large as the reported values for 4 of the 7 B vitamins.  While some brands are good sources of B1 and B6, some were quite low for these nutrients.  It seems that unfortified nutritional yeast can only be relied on to supply more than 20% daily value per serving for B2, B3, and B5:

Note that all of these unfortified nutritional yeast brands contain B5, so it seems likely that fortified nutritional yeast brands also contain this important nutrient and simply fail to test and/or report it.  However, the percent daily value for B5 is much less than that of other B vitamins in fortified nutritional yeast.  

Four of the seven unfortified brands reported B7, so it is likely that the other unfortified and fortified brands also contain this nutrient, but fail to test and/or report it.  However, even the brands that report B7 report such a small and variable amount, nutritional yeast probably cannot be relied on as a good source of this important vitamin.

Fire Frequency in Arizona Ecosystems

 

Introduction

How likely a given area is to encounter wildfire is important for planning and wildfire mitigation. Historic or expected fire return statistics are often cited for ecosystems in Arizona, but I was curious how often wildfire actually burns across different Arizona ecosystems.

Figure 1 Example wildfire polygons around Clints Well, AZ showing overlapping fires from newer (blue, labelled), to older (shades of brown, unlabelled).  Data sources include WFIGS and GeoMAC.

Wildfire Data

I used the WFIGS Interagency Fire Perimeter GIS data, which has good data on wildfires from 2000-2023.  I limited this analysis to USFS land in Arizona.

Out of a total of 11.168 million acres of USFS land in AZ, wildfire has burned 4.8 million cumulative acres in the last 24 years.  This counts areas that burned more than once as additional acres.  It includes natural and human-ignitions, as well as wildfire managed for resource benefit.  

Figure 2 Example WFIGS Interagency Fire Perimeters in AZ

Figure 3 Wildfire acreage over time in AZ.  2011 was the Wallow fire.

Vegetation Types

To evaluate wildfire probabilities in Arizona ecosystems, I looked at the 15 most common ecosystem types, as defined by the USFS Ecosystem Response Unit (ERU) vegetation type GIS layer.  Together, these 15 ecosystems account for 9.1 million out of the 11.1 million acres of USFS land in Arizona.

Figure 6 Example ERU polygons showing aspen (pink) and mixed conifer around the San Franscisco Peaks, AZ.

To calculate the percent of each ERU burned per year, I divided total acres burned by total acres of ERU and divided that by 24 years.   Spruce-Fire forest and Mixed Conifer is most likely to burn, whereas Mixed Conifer with Aspen is least likely.  Ponderosa pine ecosystems rank in the middle, at around 3% chance. 

This analysis counts acres more than once if they burned more than once in the 24 year time period.  For example, Spruce Fir Forest ERU has more acres of wildfire than there are total acres of ERU.  This does not mean that every acre burned, but some acres burned more than once.   

ERU	ERU Acres	Wildfire Acres	% burned in 24 years	% burned per year
Ponderosa Pine Forest	1,966,603	1,431,424	72.79%	3.03%
PJ Woodland	1,175,545	208,685	17.75%	0.74%
PJ Evergreen Shrub	1,136,221	311,254	27.39%	1.14%
Mojave-Sonoran Desert Scrub	779,939	386,363	49.54%	2.06%
Semi-Desert Grassland	730,015	300,189	41.12%	1.71%
Interior Chaparral	713,754	533,678	74.77%	3.12%
Juniper Grass	539,830	299,074	55.40%	2.31%
Colorado Plateau / Great Basin Grassland	367,114	41,812	11.39%	0.47%
Ponderosa Pine – Evergreen Oak	362,838	238,365	65.69%	2.74%
Madrean Pinyon-Oak Woodland	354,836	92,160	25.97%	1.08%
Mixed Conifer - Frequent Fire	349,006	304,104	87.13%	3.63%
Mixed Conifer w/ Aspen	242,169	9,782	4.04%	0.17%
Montane / Subalpine Grassland	157,163	92,461	58.83%	2.45%
Spruce-Fir Forest	112,827	124,593	110.43%	4.60%
PJ Grass	96,016	8,995	9.37%	0.39%
Madrean Encinal Woodland	93,939	23,092	24.58%	1.02%

Figure 7 ERU acres, wildfire acres, percent burned in 24 years, and percent burned per year.  Table ranked from most to least common ERU.

Wildfire Return Interval

Fire return interval is the average length of time until fire returns at a given point in the landscape.  The chance that any given acre burns depends on a large number of complex factors, including when it last burned, the topography, fuel reduction treatments, proximity to WUI and/or human use.  Still, percent burned per year in the table above (Wildfire/Year, W) can be used to calculate expected return intervals of fire, all else being equal.

Calculations – Fire per Year

To calculate expected return intervals, first calculate the probability (P) that fire will not occur in a given span of time (X).

P = (1-W)^X

For example, for Ponderosa Pine Forest over 10 years:

P = (1-0.0303)^10

P = (0.9697)^10

P = 73.5% chance that fire will not occur, or 26.5% chance that fire will occur in 10 years.

20 years:

(0.9697)^20=54% chance that fire will not occur, or 46% chance that fire will occur.

 

Figure 8 Cumulative probability of wildfire in AZ Ponderosa Pine ERU

Calculations – Fire Return Interval

If we determine a Probability, but need to know the span of time until fire occurs, we can solve for X:

P = (1-W)^X

P = log x / log (1-W)

For example, if we determine "expected return interval" to be the length of time necessary for 50% chance of fire:

 0.5  = (0.9697)^X

X = log (0.5) / log (0.9697)

X = 22 years until there is a 50% chance of fire in Ponderosa Pine Forest.

However, if we interpret "expected return interval" to be the length of time necessary for 90% chance of fire:

0.1  = (0.9697)^x

X = log (0.1) / log (0.9697)

X = 75 years until there is a 90% chance of fire in Ponderosa Pine Forest.

Over time, the probability approaches, but never actually reaches, 100% that a wildfire will occur:

Figure 9 Cumulative Probability of Fire in Ponderosa Pine ERU

Conclusion

The length of time until fire returns at a given point in the landscape depends on how certain we want to be of the chance of fire.  If we want to be very certain (90% probability), then we would expect to wait 75 years on average.  If we are OK taking the flip of a coin (50% probability), than we would expect fire to return at any given point in 22 years.  If we are risk adverse, and can only tolerate a 10% chance of fire visiting our chosen point, we should expect fire every 3.5 years, on average.

How Accurate Are Digital Thermometers?

I wanted to know my temperature, so I would know when I have a fever.  I was also curious how my temperature compares to "normal" people.  

But first, I had to figure out how accurate my digital thermometer is.

Accuracy is how close a measurement is to the true value; precision is how much variability there is in repeated measurements.  The thermometer says it is accurate to within +/- 0.2 F, but I have no way to test that because I have no other measurement of my temperature to compare it with.  I can test the precision by taking multiple readings in a row.  This usually yields the same number, indicating that the precision is very good (i.e. low variability).  However, sometimes, depending on how I hold the thermometer, the readings can vary by as much as 0.4.

What is my normal temperature?  

To test this, I took my temperature over 10 days in April, 2024.  I took 48 readings, a total of 3-4 for each hour of the day.  For each reading I lay down for a minute and then put the thermometer in my mouth, as far back under the tongue as possible.  Each reading took 90-120 seconds, so this effort included more than an hour of total time spent taking my temperature.

My average temperature is 97.4, almost a full degree less than the widely-quoted population average of 98.2.  Standard deviation is 0.6 degrees, so 68% of the time my temperature is between 96.8 and 98.  This variability include any precision variability in the thermometer.

My temperature usually reaches a minimum in the morning, rises to a maximum is in late afternoon / early evening, and then begins to fall before bedtime.  My highest temperatures were recorded after exercise, doing chores, eating a warm/fatty dinner, and lying in the sun.  Minimum temperatures were recorded lying in bed, after exercise, and after breakfast.  

Monarch Monitoring Results

 Here's a cool synthesis paper using data from the Integrated Monarch Monitoring Program (IMMP).  The headline conclusion is that ROWs had the most milkweed plants:

Site Type: ACL, Agricultural Conservation Land; DEV, Developed; PGS, Protected Grassland; ROW, Rights-of-way; UGS, Unclassified Grassland. 

However, this paper excludes Asclepias subverticillata (and A. verticillata), the most common milkweed species in our area.  Apparently this was so they could apply their total stems to the work of Thogmarten et al that calculated how many stems of milkweed are needed to support stable monarch butterfly populations.  But, since the main results of this paper compare milkweed plants between different site and habitat types, they should at least show what this analysis would look like with A. subverticillata.  

Actually, most of the paper's results are focused on Eastern U.S., and there is a companion Western paper forthcoming, but that is not obvious from the Abstract.  So excluding a common western milkweed plant may not have changed most of the results.  

What's also not obvious is that none of the figures show actual data, they only show the model results derived from the data.  Apparently that is how ecology works now-a-days:  create some kind of bespoke complicated statistical model and don't even bother to plot the underlying messy real-world data...

Citation

Front. Ecol. Evol., 23 May 2024

Sec. Conservation and Restoration Ecology

Volume 12 - 2024 | https://doi.org/10.3389/fevo.2024.1330583

How to Use Satellites to Find Growing Plants: A Practical and Theoretical Guide

USGS Maximum consecutive dry days, July 2024

Introduction

Knowing where and when plants are green and growing would help botanists and ecologists plan field work and help managers make real-time land management decisions.  There are many potential sources of geospatial data available but it is difficult to know which sources are most useful.

Over the past year I evaluated the accuracy and interpretability of dozens of indicators that could be used to assess current growing conditions.  These websites, maps, and data layers are mainly provided by US Government agencies to help managers respond to natural resource concerns such as rangeland management and drought impacts.

In my opinion, none of the available resources is a perfect fit to find growing plants.  However, understanding how plant growth is related to biophysical constraints can help identify the best available resources.

Plant Growth Theory

Plant growth (Net Primary Productivity, NPP), is proportional to total soil moisture (SM) and Growing Degree Days (GDD) of accumulated Temperature (T) since the start of that plant's growing season.  Soil moisture depends upon accumulated (total and frequency of ) precipitation (P) minus evapotranspiration (ET).  

 NPP = Sum (SM*T)

 SM = Sum (P-ET)

[In the above equations, "equals" is used to mean proportional, or depends upon. ]

The ideal resource for plant growth would be a direct measurement of NPP.  A second option would be measurement of soil moisture and temperature, with a way of combining them to estimate NPP.  A third option would be measurement of precipitation, which could be used to estimate SM. All of these variables can be measured via satellite, but delays make them difficult to use in real time.  Also, there are important details in how they are measured and modelled that make interpretation difficult.  

Details

Plant Growth: satellites can measure NPP via NDVI (Normalized Difference Vegetation Index), but available sources are delayed (DroughtView), low-resolution (GIMMS), weighted toward trees, and contain artefacts early in the growing season.  Existing models of NPP are not well-calibrated (VegDRI), or delayed and not mapped (RAP).

Soil Moisture: satellites can measure SM, but satellite data (SMAP) is not consistently available, may be inaccurate, and only provides a snapshot, not the sum.  Models of SM are not well-calibrated (NASA LIS).

Temperature: sensor networks can measure T and calculate Growing Degree Days (NPN) but aspect and micro variation are often more important than county-level temperatures.  This is helpful early in the growing season.  

Precipitation: radar and rain gauge networks can measure P and are timely and accurate.  Maps of accumulated P are available from NOAA and number of days with/without rain in the last month from USGS.  However, because P is not a direct measure of NPP, this can only be a general guide to plant growth.  

Conclusions

P - Best. Note that both total and frequency matter.

SM - not available.

T - GDD is helpful in Spring phenology, but don't matter later in the season.

NPP - difficult to use.  Good for viewing thinning and forest fires, not very helpful for spotting wildflower growth or distinguishing between grass and tree green-up.  

NOAA precipitation accumulation as percent of normal for July, 2024

Practical How-To

I recommend using a balance-of-evidence approach that combines the two best sources of precipitation information, NOAA for total accumulation, and USGS for frequency.  If both sources show good precipitation during the recent growing season, there is a good chance that plants are growing well in response to abundant and regular moisture. 

NOAA's National Water Prediction Service provides accumulated precipitation over any time period from 2005-present with less than 24 hour delay.  Their web viewer is currently the only way I know to view this data because I don't have the programming skills to use their API.  

Scroll down to Precipitation Estimate, where it is possible to set the time period of interest and map Precipitation in terms of Observed (totals), Normal (average), Departure from Normal (inches more/less than normal), and Percent of Normal.  The map is sometimes glitchy depending on connection speed.  More details about how precipitation is calculated are available in the help guide.  Note that this map is usually up to date for the previous "water day" ending in the early morning (so for AZ, selecting "Today" shows precipitation that occurred yesterday through 7 am today).

USGS's Drought Monitor provides access to several precipitation metrics, with a 48 hour delay. To assess precipitation frequency/regularity, I use Maximum Consecutive Dry Days (Past 30 days) and Days Since Precipitation.  They also offer total accumulated precipitation over the last 7 and 30 days (again, with a 2 day delay).  Their web-viewer is clunky and hard to use, but it is easily available.  Double click on the Dataset of interest in the left pane and resize the resulting window to view the area of interest.  The layers have no opacity, so to view the underlying map they have to be checked on/off in the Layers pane.  

They also make their data available as WMS that can be added to any ArcGIS desktop or online map.  The opacity can be adjusted on the Group layer that is created after the data is mapped.  However, the time-enabled settings can be difficult to use, and I haven't figured out how to show the data time period.  The web viewer is easier to change the time period and to see the data date range.

Note that USGS Drought Monitor also provides VegDRI and QuickDRI, two models that claim to incorporate all of the biophysical variables listed above (and then some!) to model NDVI difference from average.  These models are extremely complex, but they don't seem well-calibrated to Arizona because I have not found them to be very accurate or helpful.  

Cutting Trees for Water: Are Thinned Forests Wetter or Drier?

Forest thinning can be controversial.  Concerned citizens want to know when logging counts as restoration;  can thinning a forest have beneficial ecological effects beyond reducing the risk of stand-replacing wildfires?  Will cutting trees increase soil moisture because there are less "straws sucking up water", or does it decrease soil moisture due to increased windspeed and more sunlight drying out the forest understory?

April 2017 - views of Rogers Lake, AZ overlooking untreated (left) and treated (right) areas.  Photos by Conor Flynn.  Click this link to play with the slider.  

Whether thinned forests are drier or wetter is complicated.  The excellent paper "Adapting western North American forests to climate change and wildfires: 10 common questions" by Prichard et al provides a good introduction to this question:

"Decreasing canopy bulk density can change site climatic conditions (Agee and Skinner 2005). Wildfire ignition potential is largely driven by fuel moisture, which can decrease on drier sites when canopy bulk density is reduced through commercial thinning (e.g., Reinhardt et al. 2006). Reduced canopy bulk density can lead to increased surface wind speed and fuel heating, which allows for increased rates of fire spread in thinned forests (Pimont et al. 2009, Parsons et al. 2018). Other studies show no effect of thinning on surface fuel moisture (Bigelow and North 2012, Estes et al. 2012), suggesting that thinning effects on surface winds and fuel moisture are complex, site specific, and likely vary across ecoregions and seasons."

Anecdotally, some people have noticed springs beginning to flow again after thinning and prescribed fire in AZ.  My research in NM pinyon noted increased soil moisture at thinned sites (unpublished data), however this could be due to the specifics of how thinning was accomplished at those sites.  Thinned slash was chipped and the chips were left on-site without follow-up prescribed fire.

In addition to water quantity, water quality should also be considered.  Prichard et al point out that "Treatments in watersheds that are distant from the WUI and protect municipal and agricultural water supplies are critical to minimizing high-severity fire impacts that can jeopardize clean water delivery (Bladon 2018, Hallema et al. 2018). For example, post-fire erosion and debris flows may cause more detrimental and longer-term impacts to watersheds than the wildfires themselves (Jones et al. 2018, Kolden and Henson 2019)."  However, even carefully managed thinning and prescribed fire can generate excess erosion from new roads, decreased large woody debris, and increased mobility of light charred wood.  Charcoal washing into local lakes can cause fish kills, even when not generated by catastrophic wildfire.  Creating erosion-control structures as part of forest thinning work could help to mitigate these risks.  

Further research is needed to ensure that large thinning projects adequately account for water cycle restoration in addition to natural stand density and fire interval restoration.  

Rain Walks

This is a story from Paul Krafel that I think should be more widely known and celebrated.  Thank you, Paul.

Rain Walks

A simple play I’ve made hundreds of times exemplifies “every play is two plays.” High in the drainage, as runoff increases, the rising runoff begins overflowing its shallow channels, spreading out into easily overlooked overflow routes. Sometimes a rock lies in the overflow channel, obstructing how much of the runoff can flow that way. I lift the rock out of the channel so that more of the runoff can flow in this new direction (Play One). I then place that rock in the main channel so that it shunts more of the runoff towards the overflow channel (Play Two). 

This simple moving of the rock is two plays. The broader, slower overflow route receives more runoff because of the removal of the rock, and the deeper main channel receives less because of the new placement of that rock. Though much of the water still flows down the main channel, more is now flowing along the broader, slower overflow route.

Rising water has a distinctive appearance. Surface tension holds the water’s surface against plant stems and rock edges so that this ring of contact lags behind the rising level of the surrounding water. This creates a dimpled surface around each stem and rock sticking up out of the water. These dimpled surfaces sparkle with focused light. I can watch this dance of light advance with the increased flow down the overflow route.

Read more here...

Another interesting article by Paul here.