Introduction

In recent years, the question of whether horses persisted in North America throughout the Holocene, rather than going extinct at the end of the Pleistocene and being reintroduced by Europeans, has gained traction in some advocacy and public history circles. This idea was articulated in the 2017 doctoral dissertation by Dr Yvette Running Horse Collin, which argued that horses should be considered continuously native to the Americas. That dissertation and related claims circulated widely on social media and cultural blogs, prompting detailed public criticism.

One such response is the 2019 analysis on A Hot Cup of Joe titled “Pseudoarchaeological claims of horses in the Americas,” which systematically examines the evidentiary basis of post-Pleistocene survival claims and highlights methodological problems in the sources and interpretations used to support them (Feagans, 2019, https://ahotcupofjoe.net/2019/07/pseudoarchaeological-claims-of-horses-in-the-americas/). While that blog post effectively identifies many problematic assertions, it does so within the broader genre of public archaeology commentary. A comprehensive scientific evaluation requires engagement with the full body of archaeological, paleontological, radiometric, and genomic evidence, assessed according to standard disciplinary criteria.

The following analysis synthesises key lines of evidence, radiocarbon chronology, stratigraphic control, paleogenomic continuity, extinction modelling, and species distribution data to evaluate the continuous-Holocene survival hypothesis on strictly scientific grounds. It situates the public critique represented by A Hot Cup of Joe within a more formal framework of empirical support and methodological rigour, clarifying why the mainstream extinction-and-reintroduction model remains the most parsimonious explanation in the current scientific record.

The hypothesis that horses survived continuously in North America throughout the Holocene, as proposed in the 2017 dissertation by Dr Yvette Running Horse Collin, warrants evaluation according to established standards in Quaternary palaeontology, archaeology, radiocarbon chronology, and paleogenomics. Although the evolutionary origin of the genus Equus in North America is well documented, the central issue concerns the continuity of ecological conditions across the terminal Pleistocene boundary. Current scientific evidence convincingly supports a Late Pleistocene extinction of North American horses, followed by the 16th-century reintroduction of domesticated Eurasian lineages.

1. Radiocarbon Chronology and the Extinction Boundary

The North American Equus fossil record is extensive and geographically widespread during the Late Pleistocene. Thousands of specimens have been identified, with radiocarbon dates clustering between approximately 10,000 and 11,000 years before present (BP), which coincides with the terminal Pleistocene megafaunal extinction event (Faith & Surovell, 2009; Guthrie, 2006). After this extinction boundary, securely dated horse remains are consistently absent until the historic period.

Large-scale faunal occurrence databases, such as FAUNMAP II, which compile stratigraphically vetted mammalian records across North America, demonstrate a pattern of terminal Pleistocene disappearance for Equus without documented Holocene continuity (Graham & Lundelius, 2010). The extinction boundary for Equus is temporally aligned with other megafaunal losses, including mammoths and mastodons, and corresponds with wider ecological restructuring at the end of the last glacial period (Lorenzen et al., 2011).

To overturn this extinction model, one would require:

1. Securely stratified Holocene Equus remains

2. Direct radiocarbon dates on uncontaminated collagen

3. Replication across independent sites

To date, no reproducible dataset that meets these criteria has been published in the peer-reviewed literature.

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2. Stratigraphic Standards and Archaeological Control

In Quaternary archaeology, contextual integrity is fundamental. Surface finds, redeposited material, or specimens lacking documented excavation records cannot establish species continuity. Claims of Holocene survival that rely on:

* Uncontrolled provenience

* Mixed sediment layers

* Artefact association without direct dating

do not meet accepted archaeological standards. The presence of a single intrusive specimen in a disturbed context cannot overturn a continent-wide extinction pattern supported by evidence from hundreds of stratified sites.

If Holocene horses had persisted at ecologically meaningful population sizes, we would expect:

* Hunting assemblages

* Butchery marks

* Distributed osteological remains across occupation layers

Such material evidence has not been identified in securely dated Holocene archaeological contexts to date.

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3. Paleogenomic Evidence and Lineage Discontinuity

Ancient DNA analysis delivers an independent line of evidence. Sequencing of Middle and Late Pleistocene North American horses reveals distinct lineages that disappear from the record at the end of the Pleistocene (Orlando et al., 2013). Subsequent genomic analyses confirm that modern horses derive from domesticated Eurasian populations rather than from surviving endemic North American Pleistocene stock (Orlando, 2020; Bailey & Brooks, 2013).

Importantly, no ancient Holocene North American Equus specimen has yielded genomic data demonstrating continuity with pre-extinction lineages. In extinction biology, the combination of lineage discontinuity and stratigraphic absence strongly supports true extinction rather than a demographic bottleneck.

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4. Extinction Modeling and Ecological Parsimony

Late Pleistocene megafaunal extinctions are widely attributed to interacting pressures, including climatic warming, vegetational shifts, and human expansion (Barnosky et al., 2004; Lorenzen et al., 2011). Regardless of whether anthropogenic overkill, climate forcing, or synergistic mechanisms predominated, the extinction of Equus is consistent with a wider continental extinction event that affected multiple taxa simultaneously.

From a population ecology standpoint, a continent-wide species persisting undetected for 10,000 years would require:

* Viable breeding populations

* Stable habitat occupancy

* Sufficient demographic size to avoid genetic collapse

Such persistence would be expected to produce a detectable osteological and genetic signature. The absence of this evidence argues against the survival of Equus in the Holocene.

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5. Iconography and Effigy Claims

Rock art, figurines, and symbolic representations cannot independently establish species survival. Quadrupedal depictions are often morphologically ambiguous, and direct dating of petroglyphs presents methodological challenges. Even if such imagery were conclusive, cultural representation does not demonstrate biological continuity. Archaeological science requires physical, datable remains to confirm the presence of taxa.

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6. Oral Traditions and Evidentiary Categories

Oral traditions possess cultural authority and historical meaning. However, scientific reconstruction of species chronology relies on a distinct evidentiary framework. In the absence of corroborating physical remains, oral accounts cannot substitute for radiometrically dated material evidence. This distinction is methodological instead of a reflection of cultural value.

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7. Evolutionary Origin vs. Ecological Nativeness

Horses originated in North America millions of years ago and subsequently dispersed into Eurasia (MacFadden, 1992). This evolutionary origin is undisputed. However, ecological nativeness is defined by uninterrupted historical presence within a timeframe relevant to management and conservation science.

A species that becomes extinct and is later reintroduced, regardless of its ancient ancestry, does not meet the criteria for continuous ecological nativeness under standard biological frameworks.

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Conclusion

The continuous-Holocene survival hypothesis currently lacks:

* Securely dated Holocene Equus remains

* Demonstrated genetic continuity

* Stratigraphically controlled archaeological evidence linking the extinction boundary

In contrast, the extinction-and-reintroduction model is supported by radiocarbon chronology, paleogenomic analysis, faunal distribution databases, and extinction ecology theory. Unless reproducible, directly dated Holocene specimens are produced and independently validated, the terminal Pleistocene extinction of North American horses remains the most scientifically robust explanation.

Dr Meredith Hudes-Lowder ©2026

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References

Bailey, E., & Brooks, S. A. (2013). Horse genetics (2nd ed.). CABI.

Barnosky, A. D., Koch, P. L., Feranec, R. S., Wing, S. L., & Shabel, A. B. (2004). Assessing the causes of Late Pleistocene extinctions on the continents. Science, 306(5693), 70–75. https://doi.org/10.1126/science.1101476

Faith, J. T., & Surovell, T. A. (2009). Synchronous extinction of North America’s Pleistocene mammals. Proceedings of the National Academy of Sciences, 106(49), 20641–20645. https://doi.org/10.1073/pnas.0908153106

Graham, R. W., & Lundelius, E. L., Jr. (2010). FAUNMAP II: New data for North America with a temporal extension for the Blancan, Irvingtonian and early Rancholabrean (FAUNMAP II Database, Version 1.0) [Data set]. University of California Museum of Paleontology.

Guthrie, R. D. (2006). New carbon dates link climatic change with human colonization and Pleistocene extinctions. Nature, 441, 207–209. https://doi.org/10.1038/nature04604

Lorenzen, E. D., et al. (2011). Species-specific responses of Late Quaternary megafauna to climate and humans. Nature, 479, 359–364. https://doi.org/10.1038/nature10574

MacFadden, B. J. (1992). Fossil horses: Systematics, paleobiology, and evolution of the family Equidae. Cambridge University Press.

Orlando, L., et al. (2013). Recalibrating Equus evolution using the genome sequence of an early Middle Pleistocene horse. Nature, 499, 74–78. https://doi.org/10.1038/nature12323

Orlando, L. (2020). The evolutionary and historical foundation of the modern horse: Lessons from ancient genomics. Annual Review of Genetics, 54, 563–581. https://doi.org/10.1146/annurev-genet-021920-011805

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Presented below is a blog post in its entirety by Carl Feagans, an archaeologist who refutes Dr Yvette Running Horse Collin’s dissertation claims.

The blog post is located here: https://ahotcupofjoe.net/2019/07/pseudoarchaeological-claims-of-horses-in-the-americas/

“Pseudoarchaeological claims of Horses in the Americas“

July 16, 2019 Carl Feagans Archaeology 32

“Pseudoarchaeological claims of horses present in the Americas isn’t all that new. Recently, however, a new story started making the rounds on Facebook with a slightly different twist. Instead of trying to prove the introduction of the horse by the “lost tribe of Israel” or “the Philistines” as they settled the Americas a few thousand years ago, this story introduces something more plausible. Contrary to the current scientific consensus, horses, so the claim goes, didn’t go extinct in the Pleistocene and were not re-introduced post-contact. There are a lot of horse-lovers out there that get angry at people, like some cattle ranchers, who consider wild horses to be invasive species and want herds destroyed rather than compete with their cattle on public grazing lands. So I expect this fuels some of this claim.

The claim itself isn’t necessarily crazy. I find the general notion that one or more species of Equus might have survived the Pleistocene to be an interesting scientific question. But that’s not the way Yvette Collin seems to approach the issue. In fact, her PhD dissertation (Collin, 2017) from the University of Alaska Fairbanks takes a decidedly pseudoscientific approach to addressing it.

In her dissertation, Collin’s stated purpose is to “deconstruct the history of the horse in the Americas and its relationship with the Indigenous Peoples.” She seems to begin with a conclusion—that there is a “Western science” seeking to “disregard, purposefully exclude, and reconfigure” the traditional knowledge of Native Americans. Ultimately, she’d like to “reconstruct the history of the horse in the Americas in a way that is unbiased and accurate.”

Toward this endeavor, she fails.

Let’s begin with the literature review

Collin seems to miss the point of the literature review. She describes non-written forms of information transmission by Native Americans (oral tradition, wampum belts, rituals, etc.) then goes on about how Natives were persecuted and oppressed until 1978 when Carter signed the Freedom of Religion act, etc.

She mentions how historians record the introduction of horses to the Indians in the 1690s but there are Spanish records that document Indians using horses as early as 1521 in Georgia and the Carolinas. It turns out she’s citing Richard Thornton who is citing Pietro Martire (Peter Martyr) d’ Anghiera’s De Orbo Novo [The New World] (Martyr D’ Anghiera, 1912)?, written in 1530.

One immediately wonders why she didn’t cite the original source. If she had, she might’ve noticed that Thornton, a pseudoarchaeologist, tells it like he wants it known. While Thornton mentions that one of Martyr’s key informants, a captured Native American named Francisco Chicorana, could “not confirm or deny the presence of horses,” he fails to mention that Martyr also wrote of the Americas having lions and tigers and a strange elephant-like beast that we would later understand be a tapir. While it would be easy to conflate a jaguar or puma with a lion or tiger seeing one for the first time, Martyr’s mentions of the horse and Chicorana’s inability to confirm or deny their presence in Duhare (modern Geogia/Carolinas area) isn’t as Thornton misleads one to believe. Martyr writes:

In place of horses, the king is carried on the shoulders of strong young men, who run with him to the different places he wishes to visit. At this point, I must confess, that the different accounts cause me to hesitate. The Dean and Ayllon do not agree ; for what one asserts concerning these young men acting as horses, the other denies. The Dean said: “I have never spoken to anybody who has seen these horses,” to which Ayllon answered, “I have heard it told by many people,” while Francisco Chicorana, although he was present, was unable to settle this dispute. Could I act as arbitrator, I would say that, according to the investigations I have made, these people were too barbarous and uncivilised to have horses.

Collin quotes Martyr directly only to include that last line which denigrates the Native American as inferior to the white Europeans.

To further explain why a proper literature review wasn’t possible, she goes on to describe how Native oral traditions are “precise” and “convey depth and detail in a way that modern-day languages and records are unable” (Collin, 2017, p.30). Collin points out that Native American languages often have many words for concepts like rain, snow, wind, etc., implying that this is what maintains fidelity of a concept being transmitted from one generation to the next.

She omits any agenda by individuals or groups to propagandize, elaborate, embellish or change stories to keep them relevant—all present in human cultures throughout the globe in both historic and prehistoric (i.e. pre-written) periods. Though Collin does provide many 16th century accounts of early visitors to the New World recording and documenting the horses that they spotted. How do we determine which of these are accounts of horses lost by previous expeditions (escaped after being brought off ship or from ships that sunk hitting reefs or rocks) and which of these are partial or pure embellishment meant to entice European benefactors into funding additional expeditions? The horse, after all, was a resource.

What she noticeably omits in this literature review are biological, paleontological, and genetic sources of information. If one has a hypothesis regarding the existence of a mammalian species, one expects these in the literature review. No doubt Collin would have us believe that Native American oral tradition is trustworthy because cultural tradition and linguistic style was guaranteed to ensure fidelity and preclude embellishment or omission of facts inconvenient to the narrative.

Physical evidence

So what physical evidence does Collin present? Within the literature review section of her dissertation, Collin describes a 3-inch clay horse figurine that was “found on Roods Creek about 2 miles from the Chattahoochee River.” Collin also describes a small stone horse effigy that “was found in a 1974 dig ‘near the Yuchi Creek near Fort Benning, Georgia.’”

As physical evidence goes, these items are pretty much useless. First, the source she’s citing is a pseudoarchaeological book on pre-Columbian visits to the Americas by white Europeans (Farley, 1993)?. Moreover, neither of these objects are with any context that can be dated or effectively described. The first was alleged to be found by a Catholic friar; the second was by a pair of boys digging in the bank of a creek (Farley, p. 342) (Collin, p. 47).

Read Collins’ account carefully:

…and that “Manford Metcalf of Columbus, Georgia, put into my hand a small stone effigy which resembled a horse head,” which was found in a 1974 dig “near the Yuchi Creek near Fort Benning, Georgia.”

And now Farley’s original passage:

At the same 1979 symposium, Manford Metcalf of Columbus, Georgia, put into my hand a small stone effigy which resembled a horse head. He explained that his boys had found it in 1974 while digging in the side of a hill near the Yuchi Creek near Fort Benning, Georgia.”

As an archaeologist, I can tell you there’s a definite distinction between “a 1974 dig” and “boys digging in the side of a hill in 1974.” This is a blatant mischaracterization—a borderline lie—by Collins.

Rock art and geoglyphs

Collin is correct when she says rock art motifs with a horse are considered as post-contact period motifs. But it isn’t because of a “dominant Western culture” or a refusal to date the rock art in other ways. It’s because this is an effective method of dating: use of motifs. Dating rock art is hard. Not just complicated or involved hard, but really hard. Sometimes it just cannot be done. The most effective methods are relative association of a motif or stylistic element with an event or period. Often the best dates that can be had are large ranges. Occasionally, a fairly well dated element will be of a pigment that overlaps or underlays another pigment in a consistent fashion, allowing the archaeologist recording the rock art to make some basic, relative assumptions about the dates.

While it is possible to do radiocarbon analysis on rock art, it’s an extremely involved and destructive process. Pigment has to be scraped from the rock art itself. Usually, the archaeologist looks for a recent spall for a sample candidate. Then the pigment has to be separated. There’s often a binder and an emulsifier along with the pigment material itself. If the archaeologist is very lucky, she’ll have sufficient quantities of one of these elements that happens to be from a formerly living organism and will, thus, have carbon 14 isotopes within it to test.

Very often, however, no viable sample is found and no one wants to go chipping away at the rock art willy-nilly. And, then, when a sample is successfully analyzed, there’s a crazy chance of error. But this is pigment-based rock art. Petroglyphs are a whole other problem. There really isn’t any effective way to date these. It’s possible to date the patina or lichens that begin to build up in the underlying strata after the cortex of the rock is removed by pecking. But this is a pretty rare thing.

One of the ways a patina can form is through a natural process of desert varnish, which is essentially the result of a bacteria that consume manganese and iron dust. Desert varnish (DV) takes thousands of years to form, but it is possible to measure the amount of minerals (Mn, Fe, and Pb) in a petroglyph that is slowly developing that patina of desert varnish again. Using a handheld, x-ray fluorescence device one can measure the pecked portion and the unpecked portions of rock, do some math, then look the age up on a calibration curve. The downside is that the technique is still a bit experimental and requires that the minerals above be present in the natural geology of the area.

One of the rock art examples that Collin cites is from 48th Unnamed Cave in Eastern Tennessee and dates to about 6000 years BP. Captions of the image simply say, “quadruped animal,” but she assumes this is a horse. A direct date was obtained using accelerated mass spectrometry (AMS) on a sample of charcoal pigment. But the date says nothing about the species of the animal. Visually, it’s a quadruped. Instead of citing the primary source (Simek, Cressler, Herrmann, & Sherwood, 2013)? directly, Collin curiously cites an internet news source (Smith, 2013)? and criticizes that writer’s characterization of cave art in the region of East Tennessee as including “otherworldly characters, supernatural serpents and dogs…” by saying “this interpretation of the pictographs illustrating large quadrupeds accompanying people makes little sense culturally,” as if she knew what people 6000 years ago were thinking.

Had she looked at the original source, Collin would have read that the inclusion of canids (dogs, wolves, coyotes) in the local cave art was far from uncommon. And she would have seen an image that was clearly of a group of canines as example. But this might not be her actual complaint since she also says, “no dogs near the dimensional size illustrated in these pictographs were known to exist…” Collin is clearly under the mistaken belief that rock art will only include imagery that is to scale.

Another rock art example Collin uses as evidence that horses were in the Americas in pre-contact times is a pictograph element at Alto de Pitis in Peru. The description (van Hoek, 2013)? as a horse with rider dating to after 1540 is refuted by Collin based on a blog post from NephiCode.com (DowDell, 2016)?, which she agrees with. She derides van Hoek for not bothering to date the rock art without bothering to describe what dating method would possibly be appropriate for this panel. I honestly don’t know if Alto de Pitris would be a good candidate for XRF dating or not, but van Hoek did, indeed, date this element of the panel, but with relative dating based on the understood existence of the horse in Peru.

Also, among the physical evidence Collin presents for pre-contact horses are the Blythe Intaglios, a set of geoglyphs near Blythe, California in the Colorado Desert. Two of these are of quadrupeds that are generally accepted to be mountain lions. They certainly don’t resemble horses other than being quadruped. While the Blythe Intaglios are often said to be at least 1,000 years old, their actual date is unknown, mainly due to the “lack of associated time-sensitive artifacts and charcoal-bearing features” (Gilreath, 2007 p. 289).

With the geoglyphs, Collin also leans on the words of Craig Downer, who advocates for wild horses and burros to not be seen as invasive. Reviewing his bibliography for the work Collin cited, we see the familiar pattern of going to pseudoscientific sources like the magazine Ancient American—another entity, like NephiCode, with an agenda for arguing that Mormons and Jews were the ancestors of Native Americans.

Downer writes of his own discovery of alleged horse petroglyphs that he personally dated to over 1,000 years old. The method of dating? He visually compared the patina hues (Downer, 2014)?. No XRF device. No long-term study of patination and the associated color hues and patterns. No indication at all of what comprised the patina. So it is not at all surprising, Downer’s article was published in the American Journal of Life Sciences, which is listed on Beal’s List of Predatory Journals (http://bealslist.weebly.com).

Skeletal Remains

Collin continues citing fringe and pseudoarchaeological sources in her PhD dissertation to the point that it just becomes hard to trust any of her sources. There’s a variety of post-contact Equus remains that she describes and the occasional discovery that is alleged to be pre-contact. Such as the Pratt Cave excavations by Ernie Lundelius. Collin now cites another fringe writer (S. E. Jones, 2012)? who spoke of Pratt Cave, where two horse bones were recovered (a metapodial and a portion of a phalanx), both on the surface of the cave interior.

Radiocarbon dating of samples at strata below the surface revealed the oldest date in the cave to be 2820 +/- 180 years BP (Lundelius, E. L., 1979)?. That didn’t keep Jones from stating that there was a date range of 6020 to 5890 BCE for bones deposited on the surface after the samples below that dated to a maximum of about 870 BCE! Ludelius states in his paper on Pratt Cave that the surface bones were from a “small form about the size of an ass” and, although he couldn’t be sure if it was domestic or feral, it was certainly modern and likely introduced to the cave by a predator. It would seem that Jones is pulling data from thin air since he offers no citation that explains the extreme age he claims. Lundelius certainly didn’t use that age and confirmed as much in personal correspondence with me.

Not without a single surprise, Jones’ article was published first in Ancient American magazine a fringe, hyper-diffusionist periodical that features many articles about how ancient peoples in the Americas had contact and help from smarter, more technologically advanced, white people from places like Europe well before Columbus arrived. Collin also cites another set of excavated horse bones that Jones also discussed in that same article. In doing so, she cites the original publication directly but leaves out a critical observation that points to their conclusion: cut marks on the bone from a steel tool. Eckles and his colleagues acknowledge that bone occasionally produces young results (this is because of younger material clinging to buried bone) but agree that the upper limit of their obtained radiocarbon dates (after the mid 17th century) is realistic and that this is an early historic site in Wyoming (Eckles, Lockewood, Kumar, Wedel, & Walker, 1994)?.

Conclusion

Collin begins her dissertation with a clear chip on her shoulder for so-called “mainstream academia” and “Western science.” There is no “western” science. There is science. The methods of which work regardless of where you are geographically or what your ethnicity is. That’s the wonderful and marvelous thing about science is that it can be wielded by even the most oppressed or marginalized among us if its methods are adhered to. The only real trick is to observe the universe in a logical fashion and record data in a manner reasoned enough that it will provide consistent results.

While Collin rightfully pointed out the presence of bias among non-indigenous or non-Native researchers, she also pledged to overcome any bias of her own. She failed. From the outset. Her abstract revealed a conclusion that she began with and proclaimed the data she would find. No serious attempt was shown in her work to falsify her hypothesis, indeed, her null hypothesis was unclear: what would show her to be wrong as she gathered data?

The importance of having indigenous researchers and scientists around the world answering questions and exploring the heritage of their own people cannot be overstated. This is all the more reason why such an endeavor should be undertaken in a manner that places the work in a position that is as close to being beyond reproach as possible. Indeed, this should be a goal of any legitimate research endeavor.

Reliance on sources so questionable as to be considered pseudoscientific, pseudoarchaeological, and pseudohistoric, however, has the effect of diminishing any research endeavor to the fringes of science at best. It places doubt on any future work the researcher produces. And it taints the reputations of those that academically validate it. But more importantly, when it comes to advancing indigenous or historically marginalized people, such works become obstacles to those that deserve that advancement.

Collin’s dissertation cites Ancient Origins, Richard Thornton, and Dell Dowdell, and each of these sources variously or indirectly promote ideas about Native Americans which can be considered racist. Dowdell, the creator of nephicode.com, actively promotes the notion that Native Americans are the descendants of white Mormons and he believes the Earth is only as old as one of the cave paintings mentioned earlier in this article. Conspiracy theorist Richard Thornton publishes pseudoarchaeological claims of Maya settlements in Georgia. And Ancient Origins is a website that traffics in all manner of fake, fraudulent, and fantastic archaeological news, books, and media for profit. Authors they promote range from racists to general conspiracy theorists.

Coming across any one of these in a dissertation for a PhD should be enough to put all that dissertation’s sources in question. There were, perhaps, a dozen or more questionable sources of this caliber.

I’m certainly not categorically opposed to the idea that Equus may have survived the Pleistocene extinction and continues even today. This, I think is a perfectly valid, scientific hypothesis. But it’s one that should be tested using science. Not “Western science.” Not through the lens of non-indigenous academia. It should simply be tested with science, a set of methods available to anyone willing to use them regardless of geographic origin, cultural affiliation, or ethnic heritage.”

References and Further Reading

https://newsmaven.io/indiancountrytoday/news/yes-world-there-were-horses-in-native-culture-before-the-settlers-came-JGqPrqLmZk-3ka-IBqNWiQ

Collin, Y. R. H. (2017). The Relationship Between the Indigenous Peoples of the Americas and the Horse: Deconstructing a Eurocentric Myth (University of Alaska Fairbanks). Retrieved from https://search.proquest.com/docview/1895090520?accountid=6180%5Cnhttp://dw2zn6fm9z.search.serialssolution.com?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&rfr_id=info:sid/ProQuest+Dissertations+%26+Theses+Global&rft_val_fmt=info:ofi/fmt:kev:mtx:disserta

DowDell, D. (2016). The Horseman of Alto de Pitis – Part III. Retrieved from NephiCode.com website: http://nephicode.blogspot.com/2016/05/the-horseman-of-alto-de-pitis-part-iii.html

Downer, C. C. (2014). The Horse and Burro as Positively Contributing Returned Natives in North America. American Journal of Life Sciences, 2(1), 5. https://doi.org/10.11648/j.ajls.20140201.12

Eckles, D., Lockewood, J., Kumar, R., Wedel, D., & Walker, D. N. (1994). An Early Historic Period Horse Skeleton from Southwestern Wyoming. The Wyoming Archaeologist, 38(3–4), 55–68.

Farley, G. (1993). In Plain Sight: Old World Records in Anceint America. Columbus, GA: ISAC Press.

Gilreath, A. J. (2007). California Prehistory: Rock Art in the Golden State. In T. L. Jones & K. A. Klar (Eds.), Colonization, Culture, and Complexity (pp. 273–290). Lanham, MD: Rowman Altamira.

Jones, S. E. (2012, January). Were There Horses in the Americas Before Columbus? Ancient American, 5–6.

Lundelius, E. L., J. (1979). Post-Pleistocene mammals from Pratt Cave and their environmentalsignificance. In H. H. Genoways & R. J. Baker (Eds.), Biological Investigations in the Guadalupe Mountains, National Park, Texas (pp. 239–258). Washington D. C.: National Park Service, Proc.

Martyr D’ Anghiera, P. (1912). De Orbe Novo (F. (trans. . MacNutt, Ed.). Project Gutenberg reproduction ed.

Simek, J. F., Cressler, A., Herrmann, N. P., & Sherwood, S. C. (2013). Sacred landscapes of the south-eastern USA: Prehistoric rock and cave art in Tennessee. Antiquity, 87(336), 430–446. https://doi.org/10.1017/S0003598X00049048

Smith, M. (2013). Ancient Tennessee Cave Paintings Show Deep Thinking by Natives. CNN Online, June 23(Sunday). Retrieved from https://www.cnn.com/2013/06/20/us/tennessee-cave-art

van Hoek, M. (2013). The Horseman of Alto de Pitis, Peru: A Post-Columbian Outsider in a Pre-Columbian Landscape. Retrieved from https://www.academia.edu/22684628/VAN_HOEK_M._2013._The_Horseman_of_Alto_de_Pitis_Peru_A_Post-Columbian_Outsider_in_a_Pre-Columbian_Landscape._Andean_Rock_Art_Papers_-_Part_1_-_Paper_1

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A foal disappeared recently at the Salt River. There are many theories as to the cause of presumed death on line, including a mountain lion attack, posited by the Salt River Wild Horse Management Group. Despite exhaustive searches, the foal could not be found, suggesting scavenger activity. The following presents the causes of foal mortality in free-roaming horses. Given the overpopulation of the horses, reduced resources, and photographs and first-hand accounts of stallions harassing mares/foals at the river and behind the SRWHMG compound, it is possible the foal died from stallion aggression. Infanticide in wild equids, though relatively rare, is a reproductive strategy rather than an act of aggression without purpose. When a foal is killed, the mare often returns to estrus sooner than she would have if she were nursing. Normally, lactational anestrus, the period when a mare does not cycle while nursing, suppresses ovulation through elevated prolactin and reduced gonadotropin (FSH and LH) levels. Once the foal is lost, these hormonal constraints diminish, and the mare can enter estrus within days to weeks, allowing the infanticidal stallion or another bachelor a mating opportunity that would otherwise have been delayed for months.

The death rate of foals in free-roaming horse populations depends on multiple factors, which include seasonal predator attacks, population density, and fertility management systems. The most noticeable natural cause of death in free-roaming horses occurs through cougar (Puma concolor) attacks. Yet, demographic pressure from excessive population density, food scarcity, and birth control side effects leads to social changes that increase foal mortality rates.

Seasonal Cougar Predation and Sex-Specific Hunting Patterns

The MPWHT located at the California–Nevada border shows that cougars kill 70% of their foal victims before they reach three months of age, while half of all foal deaths happen before July starts. The predator did not target any adult horses during its attacks. The predation period occurred between May and June, which coincided with foal birth, while mule deer became the primary prey during the winter months (Turner JW Jr et al., 1992).

Research on kill sites showed that female cougars who had dependent kittens chose to hunt foals during this specific time, while male cougars occasionally killed adult or subadult horses but mainly focused on foals when they were abundant (Andreasen AM et al., 2021). The study in Alberta showed that female cougars primarily hunted newborns, whereas male cougars killed only adult horses in their nine recorded prey encounters (Knopff KH et al., 2010). According to the Salt River Wild Horse Management Group, a few foals have been attacked/killed by mountain lions. https://www.facebook.com/profile/100064860303576/search/?q=mountain%20lion

The same patterns of predator-prey interactions have been documented in Argentina since pumas returned to their natural habitat. The Ernesto Tornquist Provincial Park shows that 74% of foal deaths occurred during spring through summer, while 54% of the deaths involved foals under six months old and 53% showed evidence of puma predation (Bostal F et al., 2025).

The combination of excessive population numbers with insufficient food resources makes foals more vulnerable to predators.

Behind the SRWHMG’s property along the Bee-Line Highway, horses gather for food (hay and alfalfa) and water. There can be as many as 100+ horses on any given day, several of which may be pregnant or lactating. There are a few water troughs holding 100 gallons of water. Lactating mares have substantially higher water requirements due to the fluid demands of milk production—typically 50–70 litres (13–18 gallons) per day, compared with 25–45 litres (6–12 gallons) for non-lactating horses. The fact that the foal was observed at the river infrequently suggests the dam may have had limited access to water and could have been dehydrated. Insufficient water intake can reduce milk production, compromising the foal’s hydration and nutrition. In herd settings, restricted or hierarchical access to water sources—especially where dominant stallions drink first—may further disadvantage mares with foals, increasing the risk of dehydration and related stress in both dam and offspring.

Additionally, one member of the Management Group was ordered to direct horses away from Rainna, the new foal. This is incredibly dangerous because the dam is potentially in post-partum oestrus. Do not approach wild horses this closely under ANY circumstances, particularly horses that have lost their fear of humans.

Behind Salt River Management Compound along the Beeline Highway, horses have access to several (5+/-) 100-gallon water troughs. The table shows the water requirements for horses, and while they can go to the river a few miles away, most remain.

Herd size	Stallions	Non-lactating mares	Lactating mares	Foals	Gallons/day (baseline)	100 gal troughs containers needed (baseline)	Extra refills needed	Gallons/day (hot)	100 gal	Extra refills needed beyond 5 (hot)
25	11	12	1	1	253	3	0	375	4	0
30	14	12	2	2	306	4	0	450	5	0
35	16	15	2	2	356	4	0	525	6	1
40	18	18	2	2	406	5	0	600	6	1
45	20	21	2	2	456	5	0	675	7	2
50	22	24	2	2	506	6	1	750	8	3
55	25	24	3	3	559	6	1	825	9	4
60	27	27	3	3	609	7	2	900	9	4
65	29	30	3	3	659	7	2	975	10	5
70	32	30	4	4	712	8	3	1050	11	6
75	34	33	4	4	762	8	3	1125	12	7
80	36	36	4	4	812	9	4	1200	12	7
85	38	39	4	4	862	9	4	1275	13	8
90	40	42	4	4	912	10	5	1350	14	9
95	43	42	5	5	965	10	5	1425	15	10
100	45	45	5	5	1015	11	6	1500	15	10

Perinatal, Disease, and Environmental Mortality

The death of foals occurs through multiple causes, which include dystocia, trauma, failure of passive transfer, septicemia, and environmental exposure (Greger PD and Romney EM, 1999; Roelle JE et al., 2010). The combination of long-distance mare migrations to water sources leads to increased risk of foal abandonment and death from starvation and heat exhaustion. The Nevada herds experienced foal deaths when stallions attacked their young during harem conflicts, demonstrating how population density can drive social aggression that affects reproductive timing.

Bachelor Stallion Aggression and Infanticide

The excess of male horses in the population creates an unbalanced sex ratio, leading to increased aggression among stallions (Berger, 1986; Ransom, J.I., and Cade, 2009). The prolonged battles between bachelor stallions lead to band takeovers and mares experience harassment, which results in foal injuries or deaths through trampling and targeted attacks during harem disruptions (Roelle JE et al., 2010; Greger PD and Romney EM, 1999). The behaviour of killing unrelated foals by new males is an adaptive yet destructive practice observed in other equid species.

Stallions become more aggressive because they have limited access to mares, and their poor nutritional state creates social instability in overpopulated systems. The prolonged stress from harassment causes mares to develop elevated cortisol levels, which leads to reproductive suppression and breaks down social bonds between band members, thus worsening demographic outcomes (Ransom JI et al., 2014).

The video below demonstrates bachelor aggression towards a young foal. It may upset some viewers; however, the foal was fine.
The entire post is here

Fertility Control: PZP Versus GonaCon and Behavioural Implications

The implementation of fertility control measures for non-lethal herd management affects social structures and death rates.

The PZP fertility control method used in free-roaming horse populations stops fertilisation but creates delays in conception and disrupts foaling synchronisation. The extended predation period results from delayed foaling times because PZP contraception leads to more miniature foals who become vulnerable to cougar attacks (Boyce PN and McLoughlin PD, 2021). The GnRH immunocontraceptive GonaCon functions as a reproductive hormone suppressor, affecting both male and female animals to prevent estrus and delay or stop reproductive cycles. The effectiveness of GonaCon in reducing foaling rates leads to different social changes than PZP does. The suppression of reproductive hormones by GonaCon creates stable band behaviour because it eliminates sexual cues, which reduces stallion competition and decreases the chances of aggression and infanticide. The extended period of reproductive behaviour suppression induced by GonaCon treatment might cause stallions to leave their non-receptive mares, as they lose their natural bonding signals.

Research findings on these vaccines remain scarce, but observations indicate that GonaCon-treated horse populations exhibit less aggressive behaviour than PZP-treated populations during the breeding season (Ransom JI et al., 2014). The high population density creates extreme competition between animals, which leads to foal injuries and deaths even when using different contraception methods. The combination of hormonal suppression with GonaCon treatment helps control foal deaths from infanticide and injuries, but does not stop them from occurring when animals face extreme population pressure.

Integrated Interpretation

The combination of seasonal predator attacks, density-related food scarcity, and reproductive social patterns leads to the death of foals in free-ranging horse populations. The Salt River’s overpopulation creates conditions where female cougars can predictably hunt foals. At the same time, male aggression and competition for resources lead to additional foal deaths through social conflicts and physical harm. The selection of fertility control methods affects social dynamics because PZP prolongs foal exposure to danger through delayed births, but GonaCon decreases aggressive and infanticidal conduct by blocking oestrous cycles. Successful population management requires three essential components: reducing population density, selecting fertility control methods based on behavioural effects, and conducting ongoing assessments of both population statistics and animal well-being.

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Summary of Relative Mortality Causes (Most to Least Significant)

1. Predation by cougars, concentrated in late spring–early summer, accounts for the majority of documented foal deaths (<3 months).
2. Nutritional limitation and dehydration from overpopulation, drought, and poor forage quality, leading to starvation and higher predation vulnerability.
3. Social aggression and infanticide, amplified by overpopulation and foal heat estrus, with added risk under PZP contraception.
4. Perinatal complications, including dystocia, birth trauma, and maternal exhaustion.
5. Environmental exposure, accidental injury, and infectious disease are often secondary to other stressors.

Management Implications:

• Reduce herd density toward ecological carrying capacity (100–200 animals for Salt River).
• Prioritise GonaCon over PZP in high-density, aggression-prone herds to suppress foal heat and reduce infanticide.
• Coordinate fertility control with habitat restoration, water management, and behavioural monitoring.
• Recognise that effective management must balance population control, social stability, and foal welfare across ecological and behavioural dimensions.

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In summary:
Foal mortality in overpopulated free-roaming herds arises primarily from predation, followed by nutritional stress, social aggression and foal heat–related infanticide, perinatal complications, and environmental or infectious factors. Overpopulation magnifies all mortality pathways. Management strategies emphasising density reduction, behaviorally informed fertility control (GonaCon), and habitat restoration offer the best prospects for improving foal survival and ecological balance.

The Salt River herd experiences higher foal death rates because of its dense population, which creates multiple factors that lead to increased mortality. The competition for resources between horses and their environment leads to poor mare body condition, which weakens foal health and raises their risk of dying early. Research on free-roaming horses shows that population density and restricted habitats lead to lower foal survival rates.

The Salt River Wild Horse Management Group reports that Salt River foal survival rates reach only 70% during their first year of life. Salt River Wild Horse Management Group. The Salt River Wild Horse Management Group observes that foal deaths occur because of social conflicts between bachelor stallions, which become more severe when band numbers increase in densely populated areas. (https://saltriverwildhorsemanagementgroup.org/on-this-foal-friday-we-have-good-news-and-bad-news/?)

The combination of high animal numbers at water sources creates conditions that increase foal dehydration and trauma risks, according to research on feral horses and ungulates, which shows density affects juvenile survival through maternal health, resource competition, and environmental stress.
The current evidence indicates that Salt River herd overpopulation leads to higher foal mortality rates, making density management essential to protect foal survival and maintain herd health.

References

1. Turner JW Jr, Wolfe ML, Kirkpatrick JF. Seasonal mountain lion predation on a feral horse population. Can J Zool. 1992;70:929-934.
2. Andreasen AM, Stewart KM, Longland WS, Beckmann JP. Prey specialization by cougars on feral horses in a desert environment. J Wildl Manag. 2021;85:1104-1120.
3. Knopff KH, Knopff AA, Kortello A, Boyce MS. Cougar kill rate and prey composition in a multiprey system. J Wildl Manag. 2010;74:1435-1447.
4. Bostal F, Scorolli AL, Zalba SM. The comeback of a top predator and its effects on a population of feral horses. Perspect Ecol Conserv. 2025;23:121-129.
5. Arizona Department of Agriculture. Salt River Horse Management Plan. Phoenix, AZ: AZDA; 2025.
6. Greger PD, Romney EM. High foal mortality limits growth of a desert feral horse population in Nevada. Great Basin Nat. 1999;59:374-379.
7. Roelle JE, Singer FJ, Zeigenfuss LC, Ransom JI, Coates-Markle L, Schoenecker KA. Demography of the Pryor Mountain Wild Horses, 1993–2007. US Geol Surv Sci Invest Rep. 2010-5125.
8. Berger J. Wild Horses of the Great Basin: Social Competition and Population Size. Chicago, IL: University of Chicago Press; 1986.
9. Ransom JI, Cade BS. Quantifying equid social behavior: a review of current research and future directions. Appl Anim Behav Sci. 2009;120:1-11.
10. Ransom JI, Kaczensky P, Lubow BC, et al. Influence of demography and social stress on feral horse reproduction. J Wildl Manag. 2014;78:916-925.
11. Kirkpatrick JF, Turner JW Jr. Achieving population goals in wild horses with fertility control: science and social context. Wildl Soc Bull. 2011;35:102-110.

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Comparative Analysis of Quadrupedal Gallop and Gait Studies: The rotary gallop

Galloping in quadrupeds has been extensively studied to understand the mechanics, energetics, and evolutionary adaptations of various species. The selected studies examine the differences between transverse and rotary gallops, the role of body morphology, such as centre of mass offset, in mechanical models of locomotion, and the application of gait analysis in horses and other cursorial mammals. The rotary gallop is more commonly observed in animals with flexible backbones, such as cats (including cheetahs) and certain dogs such as greyhounds and whippets. We examined thousands of our photos and found two mustangs using a rotary gallop.

Bertram and Gutmann examined the fundamental mechanics of gallop and identified a crucial difference between transverse and rotary forms. Horses epitomize the transverse gallop, where the hindlimbs initiate the center of mass directional transition, a dynamic compared to a skipping stone. Cheetahs use a rotary gallop, with the forelimbs starting the motion like in human running.  This distinction highlights how each gallop type optimizes momentum transfer and energy efficiency in relation to species-specific
morphology and performance requirements¹.

Yamada et al. further investigated gallop selection through a modeling study focused on horses. Their work demonstrated that the anterior offset of the horse’s center of mass enhances stability and makes the transverse gallop more effective at high speeds. Simulations confirmed that transverse gallop is mechanically optimal given the horse’s morphology. This supports the idea that body structure, particularly center of mass placement, constrains and dictates gait choice across species².

Parra et al. compared two leg dynamic models—MMS (Mass-Moment-Spring) and SLIP (Spring Loaded Inverted Pendulum)—in the context of galloping quadrupeds. Their results showed that rotary gallop species, such as cheetahs and greyhounds, exhibited higher bending moments and greater capacity for elastic energy storage, enabling rapid acceleration. Conversely, transverse gallop species, including horses and alpacas, produced greater maximum bending moment at forelimb initiation, emphasizing stability and endurance. Importantly, the MMS model, which accounts for leg mass, provided more accurate representations of trunk mechanics than the SLIP model³.

Barrey reviewed the methods, applications, and limitations of equine gait analysis, focusing on the use of kinetic, kinematic, and accelerometric approaches. Gait analysis was shown to be essential in quantifying lameness and evaluating training effects, with stride length and frequency strongly correlated to physiological responses. While laboratory-based tools such as force plates and accelerometry provided detailed insights into gait asymmetry, the translation of these advanced techniques into practical field applications remained limited. Nonetheless, the study emphasized that equine biomechanics has matured into a discipline with important clinical and performance applications⁴.

Together, these studies highlight that gallop type is closely linked to limb initiation strategy and body morphology. Horses, with an anterior centre of mass, favour the transverse gallop for stability and endurance, while cheetahs employ the rotary gallop for speed and agility. Comparative modeling demonstrates how mechanical structures drive these differences, and gait analysis provides practical tools for applying biomechanical insights in veterinary medicine and performance training.

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Below a stallion gallops across Sand Wash Basin in 2015 utilizing a rotary gallop. In photo 4, you can see the rotary stride.

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The bay stallion below from Little Book Cliffs, demonstrates the extended stride of a rotary gallop.

In the image below, the stallion could either be jumping over an obstical, or about to extend both forelegs in a rotary stride.

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Prevalence in horses

Breed

• Most horses use the transverse gallop almost exclusively.
• Rotary gallop is typical of extreme speed specialists like greyhounds and cheetahs. In horses, it shows up rarely, usually at maximal effort.
• Some anecdotal reports suggest Thoroughbreds (racehorses) are more likely to show a rotary sequence when pushed to top racing speed, but formal kinematic studies show the transverse gallop dominates across equine breeds (including Thoroughbreds, Arabians, Standardbreds, and Quarter Horses).

Age

• Foals sometimes experiment with rotary sequences in play, since they try out a variety of limb coordination patterns before settling into the adult repertoire.
• Adults overwhelmingly use transverse gallop, unless pushed into unusual conditions (fatigue, uneven terrain, or sprinting).

Gender (Sex)

• No evidence that mares, geldings, or stallions differ in gait type. The mechanics are biomechanical, not gender-related.

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References

1. Bertram JEA, Gutmann A. Motions of the running horse and cheetah revisited: fundamental mechanics of the transverse and rotary gallop. J R Soc Interface. 2008;6(35):549-559. doi:10.1098/rsif.2008.0328.
2. Yamada T, Aoi S, Adachi M, Kamimura T, Higurashi Y, Wada N, Tsuchiya K, Matsuno F. Center of mass offset enhances the selection of transverse gallop in high-speed running by horses: a modeling study. Front Bioeng Biotechnol. 2022;10:825157. doi:10.3389/fbioe.2022.825157.
3. Parra EA, García-Díaz V, Díaz-Rodríguez M, Quintero JE. Comparison of leg dynamic models for quadrupedal running: MMS vs. SLIP. Sci Rep. 2022;12:14579. doi:10.1038/s41598-022-18536-7.
4. Barrey E. Methods, applications and limitations of gait analysis in horses. Vet J. 1999;157(1):7-22. doi:10.1053/tvjl.1998.0301.

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Pre-domestication (Wild Horses, 35,000–10,000 years ago)

The Early Domestication period at Botai

The Early Domestication period at Botai spanned from 3500 BCE to 3000 BCE. The three primary coat colours of bay, black, and chestnut spread widely while dun remained present but became less common (Outram et al., 2009).

The Bronze Age spanned from 3000 BCE until 1000 BCE.

The domestic horse population started to develop new genetic traits, which included the cream dilution (CR) responsible for palomino and buckskin colours and the silver dilution (Z) that lightens black pigmentation and creates flaxen manes and tails (Reissmann & Ludwig, 2013). The presence of pinto spotting (tobiano/overo) in horses from Hungary and Siberia became evident through ancient DNA analysis during the time period between 2500 BCE and 2000 BCE (Ludwig et al., 2009). The research of Wutke et al. (2016) discovered tobiano spotting in horses from Botai, Kazakhstan, and Germany, which dates back to 3600–3300 BCE and 3300 BCE, respectively, showing that tobiano existed during the first domestication period. The first appearance of polygenic white markings, which include stars, blazes, socks, and stockings, occurred in Bronze Age genomes as evidence of early selection for decorative traits (Pruvost et al., 2011).

The Iron Age and Antiquity (1000 BCE to 500 CE)

The STX17 duplication, known as the grey mutation, emerged between 200 BCE and 200 CE, and then spread quickly because people valued its visual appeal and symbolic meaning (Royo et al., 2008). The grey horse colouration emerges at birth but develops depigmentation as the animal ages, which distinguishes it from previous stable genetic mutations. The frequency of sabino and tobiano spotting patterns rose significantly during the Late Bronze and Iron Ages until tobiano reached its peak at 19% in Iron Age horses (Wutke et al., 2016). The depiction of pinto and leopard-spotted horses in Scythian, Celtic, and Roman art from ancient times demonstrates that patterned horse coats became widespread throughout Eurasia (Ludwig et al., 2009).

The Early Modern Period (500 CE to 1500 CE).

The research by Wutke et al. (2016) demonstrates that the occurrence of spotted and diluted coat patterns decreased dramatically throughout the Middle Ages, while solid coat colours, particularly chestnut, became more prevalent. The Roman Empire’s collapse and subsequent decline in breeding practices reduced the need for horse identification. Additionally, possible religious and cultural meanings associated with apocalyptic riders and church art may have contributed to this colour shift. The pearl dilution allele first appeared in medieval Iberian, German, and Slovakian horses, according to Wutke et al. (2016), but its presence remained limited to Iberian regions. The Iberian and baroque breeds maintained their preference for grey horses while Spanish-introduced leopard-spotted horses evolved into the contemporary Appaloosa breed (Wutke et al., 2016). The period between 1500 CE and the present day marks the beginning of the Post-Medieval to Modern era.

The roan pattern emerged as a result of the KIT allele, but scientists discovered it through European draft breeds before it spread to American stock horses (Holl et al. 2019). The KIT gene mutation SB1 leads to irregular white markings on legs and face with roaned edges, which scientists discovered in Clydesdales and Tennessee Walkers during the domestication period (Haase et al. 2007). The genetic basis of Rabicano remains unknown; however, scientists agree it developed during the domestication period because it affects Arabians and Thoroughbreds (Imsland et al. 2015). The genetic basis of flaxen remains unknown because it lightens chestnut manes and tails through multiple genetic factors. At the same time, sooty creates dark hair patterns on the topline, which scientists first documented during modern times (Holl et al. 2019). The mushroom dilution mutation in Shetland ponies results from a recessive MFSD12 mutation, which produces a sepia-colored chestnut coat with lightened mane and tail, and scientists believe it emerged recently (Ishida et al. 2015). The champagne dilution mutation in SLC36A1 occurred within the past few hundred years and now affects many American stock and gaited breeds (Cook et al. 2008). The pearl gene functions as a recessive dilution factor, which comes from Iberian origins and produces pale coat colours when horses have two copies of the gene or when they carry the cream gene (Wutke et al. 2016). The Dominant White alleles represent the newest group of mutations, which include more than 30 independent KIT mutations that result in white or nearly white foals at birth, and these mutations emerged during the last few centuries from specific founder horses in Thoroughbreds, Arabians, and Quarter Horses (Haase et al. 2009).

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Chronological Order of Horse Coat Colors

Pre-domestication (Pleistocene / Ice Age Horses, >30,000 years ago)
Bay (wild-type) — ancestral color.
Dun (primitive dilution with dorsal stripe, leg barring).
Black (MC1R mutation).
Chestnut (MC1R e/e mutation).
Leopard complex (Appaloosa spotting, LP) — confirmed ~25,000 years ago (Pech-Merle cave horses).
Pangaré (mealy shading, light muzzle/belly, primitive modifier).
Sooty (countershading/black hairs through coat).

Early Domestication (Botai ~3500–3000 BCE)
Polygenic white markings (stars, blazes, socks, stockings).
Tobiano spotting — detected in Eneolithic/Copper Age horses (~3600–3300 BCE; Botai and Germany)

Bronze Age (3000–1000 BCE)
Cream dilution (CR) — palomino, buckskin.
Silver dilution (Z) — dilutes black, light mane/tail.
Sabino-1 (SB1, KIT) — introduced after domestication; confirmed in Bronze Age samples.

Iron Age / Antiquity (1000 BCE–500 CE)
Grey (STX17 duplication) — appears ~200 BCE–200 CE.

Medieval (500–1500 CE)
Pearl dilution (prl, MATP gene) — first detected in medieval Iberian and European horses

Post-Medieval to Modern (1500 CE–present)
Roan (KIT allele) — absent in ancient DNA, appears in European draft breeds, later in stock breeds.
Rabicano — flank/tailhead roaning, genetic basis unknown, only in domestic horses.
Flaxen — light mane/tail on chestnut; polygenic modifier.
Mushroom (MFSD12 mutation) — sepia chestnut, Shetland ponies.
Champagne (SLC36A1 mutation) — American stock and gaited breeds.
Dominant White (multiple KIT mutations, W1–W30+ ) — independent, breed-specific, recent mutations.

Summary
Oldest: Bay, dun, black, chestnut, leopard complex (Ice Age horses).
Early domestication (~3500 BCE): white markings, tobiano.
Bronze Age: cream, silver, sabino.
Iron Age (~200 BCE): grey.
Medieval (~500–1500 CE): pearl.
Modern (1500 CE–present): roan, rabicano, flaxen, mushroom, champagne, dominant white.(Palomino, Buckskin, Smokey Black),

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Dr. Meredith Hudes-Lowder
September 13, 2025

References

• Cook, D., Brooks, S., Bellone, R., & Bailey, E. (2008). Missense mutation in exon 2 of SLC36A1 responsible for champagne dilution in horses. Genomics, 92(2), 93–98.
• Haase, B., Brooks, S. A., Schlumbaum, A., Azor, P. J., Bailey, E., Alaeddine, F., … Poncet, P. A. (2007). Allelic heterogeneity at the equine KIT locus in dominant white (W) horses. PLoS Genetics, 3(11), e195.
• Haase, B., Rieder, S., Tozaki, T., & Brooks, S. A. (2009). Five novel KIT mutations in horses with white coat colour phenotypes. Animal Genetics, 40(5), 623–629. https://
• Holl, H. M., Brooks, S. A., Bailey, E., & Mack, M. (2019). Equine coat color genetics. In T. R. Famula, E. Cothran, & M. Bowling (Eds.), Equine Genetics (2nd ed., pp. 83–104). Wiley.
• Imsland, F., Reissmann, M., & Andersson, L. (2015). Epistatic and pleiotropic effects of coat colour genes in horses. Animal Genetics, 46(5), 407–417.
• Ishida, N., Hasegawa, T., Takeda, K., Sakagami, M., Onuki, A., Inoue-Murayama, M., … Mukoyama, H. (2015). A frameshift mutation in the MFSD12 gene is associated with the mushroom coat color dilution in Shetland ponies. BMC Genetics, 16, 101.
• Ludwig, A., Pruvost, M., Reissmann, M., Benecke, N., Brockmann, G. A., Castaños, P., … Hofreiter, M. (2009). Coat color variation at the beginning of horse domestication. Science, 324(5926), 485.
• Outram, A. K., Stear, N. A., Bendrey, R., Olsen, S., Kasparov, A., Zaibert, V., Thorpe, N., & Evershed, R. P. (2009). The earliest horse harnessing and milking. Science, 323(5919), 1332–1335.
• Pruvost, M., Bellone, R., Benecke, N., Sandoval-Castellanos, E., Cieslak, M., Kuznetsova, T., … Ludwig, A. (2011). Genotypes of predomestic horses match phenotypes painted in Paleolithic cave art. Proceedings of the National Academy of Sciences, 108(46), 18626–18630.
• Royo, L. J., Álvarez, I., Beja-Pereira, A., Molina, A., Fernández, I., Jordana, J., Gómez, E., Goyache, F., & Cañón, J. (2008). The origins of the grey phenotype in horses. Nature Genetics, 40(8), 1004–1007.
• Reissmann, M., & Ludwig, A. (2013). Pleiotropic effects of coat colour-associated mutations in humans, mice and other mammals. Seminars in Cell & Developmental Biology, 24(6–7), 576–586.
• Wutke, S., Benecke, N., Sandoval-Castellanos, E. et al. Spotted phenotypes in horses lost attractiveness in the Middle Ages. Sci Rep 6, 38548 (2016). https://doi.org/10.1038/srep38548

Dr Meredith Hudes-Lowder: Biostatistician

Historical perspective

Free-roaming horse herds in the West are combinations of domestic lineages, not unique taxa. Peer-reviewed studies and federal reports indicate that U.S. free-roaming herds are formed from various breeds and sources, including ranch stock and tribal herds. For instance, a genetic analysis of Theodore Roosevelt National Park, a well-documented example, reveals that Western rangelands have historically received horses from diverse origins and breeds (Thomas, 2024). Horses in the park likely originated from multiple sources, a typical pattern for U.S. feral herds.

The National Academies’ review of BLM genetics lists a few herds with evidence of old Spanish bloodlines, such as those in the Cerbat Mountains of Arizona, the Pryor Mountains of Montana, and Sulphur in Utah. Salt River is not on this list, highlighting that it is not a distinct genetic population that needs specific preservation. National Park Service sites consistently describe free-roaming horses as non-native, feral descendants of domestic stock, not as a separate wildlife species.

“Extinction” is not the right term; the real risk is local extirpation. By definition, extinction refers to the worldwide loss of a species, whereas extirpation denotes the disappearance of a species from a particular area, even as it continues to exist elsewhere. If a local herd disappears, that means it has been extirpated, not extinct. Looking at horses: the domestic species Equus caballus is plentiful, with about 6.65 million domestic horses in the U.S. (2023 American Horse Council) and around 73,000 free-roaming horses and burros on BLM lands (2025). A local decline at Salt River cannot result in species-level “extinction.”

If genetic health is the concern, it can be addressed without exaggerating census numbers. Standard conservation practice for feral herds involves monitoring diversity and, if necessary, introducing outside genetic stock. This is precisely what peer-reviewed studies suggest when diversity is low, such as in TRNP. In other words, genetic viability is managed through breeding contributions and gene flow, not by claiming a unique lineage at Salt River.

In summary, the Salt River herd is a historical grouping of formerly domestic horses, not a unique taxon. Federal reports recognize only a few Spanish-lineage herds elsewhere. Using accurate terminology, the worst-case biological outcome is local extirpation, not extinction. Even local genetic risks are manageable with standard tools, such as monitoring and, if needed, introductions.

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Introduction: The genetic problem we’re solving

The Salt River Herd’s long-term health depends on maintaining enough genetic diversity to adapt to drought, disease, and habitat change. The single best indicator of how fast diversity erodes is adequate population size (Ne)—the number of animals that actually pass genes to foals—rather than the raw headcount (N) (Waples et al., 2013; Nunney, 1993). In free-ranging, polygynous horses, Ne is usually lower than N because a few stallions sire a disproportionate share of foals, and age/sex structure is uneven (Waples et al., 2013; Nunney, 1993). Modern conservation guidance therefore manages to set Ne targets explicitly—keep Ne ≳ 50 to limit short-term inbreeding and push higher when possible for long-term adaptability—rather than working to census alone (Frankham et al., 2014a, 2014b). We will treat genetic diversity as a routine management outcome and show it on the same dashboard as foaling rate and habitat metrics (Hoban et al., 2021; Andersson et al., 2022).

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What determines genetic viability (and why headcount isn’t enough)

Who breeds matters more than who is present. A manager-friendly relation explains why male monopolies depress Ne:

Here, Nm and Nf are the numbers of breeding stallions and mares, respectively (Waples et al., 2013; Nunney, 1993). If only a few stallions dominate paternity, Ne stays low no matter how many mares are on the landscape. Genetic drift then chips away at diversity every generation, which is why holding Ne near or above ~50 measurably slows loss across horse-length timeframes (Frankham et al., 2014a). Conversely, spreading reproduction across more stallions and enough mares lifts Ne at the same census size. For example, moving from roughly 12 breeding stallions/35 breeding mares (Ne ≈ 36) to ~20 breeding stallions/40 breeding mares (Ne ≈ 53) crosses the short-term safety threshold without increasing total herd size—purely by broadening who contributes (Waples et al., 2013).

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What can we do to maintain the genetic health of the Salt River?

Measure smart, noninvasively. We will establish a genetic baseline using fecal DNA (fresh, air-dried in paper bags), then re-sample every two years. Labs will pre-screen extracts by qPCR, use replicate genotyping, and track error rates to ensure reliability (King et al., 2018; Hausknecht et al., 2010). Routine outputs: Ne (linkage-disequilibrium estimators), heterozygosity, allelic richness, and—when blood/tissue SNPs are available—runs of homozygosity (ROH) to detect recent inbreeding (Colpitts et al., 2022; Andersson et al., 2022).

Manage breeding probabilities (without assigning mates).

Broaden stallion contribution: use contraception and removal priorities to prevent a few males from monopolizing paternity; the goal is ~18–22 effective breeding stallions alongside ~35–40 breeding mares, which typically yields Ne ≈ 50–55 at N ≈ 100 (Waples et al., 2013; Nunney, 1993).

Rotate contraception by lineage: keep contraception on standard lines; off for 1–2 seasons for under-represented lines so their foals enter the crop (Nuñez et al., 2017).

Pick the right tool, measure the tradeoffs: PZP often extends receptive behavior and increases mare band-switching (lower mare fidelity), reshuffling who breeds (Nuñez et al., 2009; Nuñez et al., 2010; Madosky et al., 2010; Jones & Nuñez, 2019). GnRH immunocontraception (e.g., GonaCon-type) can suppress fertility with no deleterious breeding-season behavioral effects when implemented carefully (Ransom et al., 2014). We will track mare band-change rates and the number of effective breeding stallions alongside genetic metrics to adjust dosing in real-time (King et al., 2021; Nuñez et al., 2017; Jones et al., 2020).

Avoid genetically biased removals: when removals/adoptions are required, retain animals carrying rare alleles or representing under-sampled lines. Use pedigree-aware tools such as PMx where pedigrees exist, or molecular kinship rules where they do not (Lacy et al., 2011; Putnam & Ivy, 2014).

Use “natural” gene flow when available: occasional 5–10% cross-boundary inflow from neighboring jurisdictions (e.g., Fort McDowell Yavapai Nation; Salt River Pima–Maricopa Indian Community) is not guaranteed but has occurred; when newcomers appear, document immediately, pause contraception on immigrant mares, and avoid removing immigrant stallions until they’ve contributed foals—this raises NmN_mNm​ and lowers variance in family size, boosting Ne (King et al., 2018; Waples et al., 2013; Nunney, 1993).

If trends still slip: implement small, screened genetic rescue (a few unrelated immigrants over several years) using standard outbreeding-risk safeguards (Frankham et al., 2011; Frankham et al., 2014a, 2014b).

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Refuting the “low numbers = extinction” claim “

Too few horses means inevitable extinction” is incorrect because extinction risk is not determined by census size alone. What matters genetically is Ne, which we can measure and manage. A herd of ~100 can maintain a population of 50 or more when reproduction is spread across around 20 stallions and 35–40 mares (Waples et al., 2013; Nunney, 1993). Ongoing noninvasive monitoring lets us detect drift or inbreeding early and adjust contraception/removals accordingly (King et al., 2018; Andersson et al., 2022). If needed, even small gene flow—natural cross-boundary movement or a carefully screened introduction—has repeatedly improved diversity and fitness in small populations (Frankham et al., 2011; Frankham et al., 2014a, 2014b). The cautionary case is closed, isolated systems like Sable Island, which persist at modest size but accumulate ROH and inbreeding over time—underscoring that management plus occasional gene flow, not inflated census, is the remedy (Plante et al., 2007; Colpitts et al., 2022; Colpitts, 2024). In short: a well-managed, moderately sized Salt River Herd can remain genetically viable without overshooting ecological limits, provided we monitor Ne and act on the results. Why genetics matter. 

Genetic diversity is crucial for a free-roaming herd’s ability to adapt to internal and external forces. It helps maintain options when weather, disease, or habitat conditions change. The most useful measure for tracking how quickly this capacity declines is effective population size (Ne). This number represents the animals that actually pass genes to foals. In free-ranging horses, Ne is often lower than the total number of horses. This occurs because a few stallions sire many foals and there is an uneven distribution of age and sex classes (Waples et al., 2013; Nunney, 1993). Modern recommendations set clear Ne targets. Keep Ne at or above 50 to reduce short-term inbreeding and increase it when possible for long-term adaptation. Genetic diversity should be a routine management goal, not an afterthought (Frankham et al., 2014a, 2014b; Hoban et al., 2021; Andersson et al., 2022).

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What drives Ne, the number of reproducing horses?

Ne relies on who breeds, not just who is present. There is a practical connection between Ne and the number of breeding males and females. This highlights the negative impact of male monopolies: 

So, if only a few stallions (Nm) dominate offspring production, Ne remains low regardless of how many mares (Nf) foal (Waples et al., 2013; Nunney, 1993). Genetic drift reduces variation with each generation. Keeping Ne at or above about 50 significantly slows this decline across generations (Frankham et al., 2014a). The expected decrease in heterozygosity due to drift is given by:

 

Short-term bottlenecks have a significant impact because the harmonic mean influences long-term Ne: 

Variance in family size, such as a few stallions producing many foals, further lowers Ne like this: 

(Nunney, 1993; Waples et al., 2013).   

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How to measure all of this without roundups. 

Fresh fecal pellets contain enough sloughed cells for individual identification and population-genetic studies. Air-drying these pellets in paper bags often yields better results than using ethanol for PCR, especially in arid areas (King et al., 2018). Accuracy improves if labs pre-screen DNA extracts using qPCR to filter out low-quality samples and implement replicate genotyping with negatives to minimize errors (Hausknecht et al., 2010). Aside from classic measures such as heterozygosity and allelic richness, it can also include user-friendly indicators on dashboards to keep non-genetic audiences informed about progress (Andersson et al., 2022; Hoban et al., 2021). We plan to include this in our Salt River Horse Registry Database^tm or other similar monitoring programs at other management areas. When blood or tissue samples are available, genome-wide SNPs allow for the calculation of runs of homozygosity (ROH) and a genomic inbreeding fraction: 

This is sensitive to recent inbreeding and small Ne (Colpitts et al., 2022). Cases from Sable Island illustrate this pattern. Early microsatellite studies described diversity within a closed herd. Later ROH mapping showed significant genomic markers of drift and inbreeding. A recent dissertation links those genomic trends to demographics and ecology, with clear implications for management (Plante et al., 2007; Colpitts et al., 2022; Colpitts, 2024). Small and isolated herds on Greek islands demonstrate how geography and history can rapidly lead herds to different genetic baselines. This means that goals and strategies should consider the local context (Katsoulakou et al., 2023).  

Total round costs of Genetic Analysis

Two ready-to-use budgeting scenarios

A grant could be obtained for the studies

A) Baseline genetics (Salt River–style), n = 80 fecal samples

• Extraction (80× $16) ≈ $1,280. fees.oregonstate.edu
• qPCR prescreen (plate-based; modest line item). PMC
• Microsat PCR + fragment analysis + scoring (assume $60–$120 per successful sample) → $4,800–$9,600. The University of Alabama at Birmingham and Texas A&M University-Corpus Christi
• Baseline lab subtotal ≈ $6.1k–$11.0k (add reporting/PI time as needed).
  (If you outsource end-to-end to a wildlife genetics provider, expect a single per-sample quote that folds these in.)

B) Monitoring cycle, n = 50 fecal samples

• Extraction ≈ $800. fees.oregonstate.edu
• Microsat + scoring (assume $60–$120 each) → $3,000–$6,000. The University of Alabama at BirminghamTexas and A&M University-Corpus Christi
• Monitoring lab subtotal ≈ $3.8k–$6.8k.

If you add hair/blood SNP arrays on a handled subset, reagent list prices start at $35 per array (processing extra) and require high-quality DNA; plan $100–$200+ per sample all-in for service providers. Use arrays to map relatedness/ROH, but rely on microsats for routine fecal rounds. Neogen and Thermo Fisher Scientific

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Contraception, social structure, and genetics. 

Contraception can support genetic goals, but different methods can affect social structure, which impacts the equations above through Nm and Vk. PZP prevents fertilization and is associated with more extended periods of receptivity and increased band-switching (lower mare fidelity). This reshuffle mating opportunities and often increases male competition (Nuñez et al., 2009; Nuñez et al., 2010; Madosky et al., 2010; Jones & Nuñez, 2019; Jones et al., 2020). GnRH immunocontraception, like GonaCon-type programs, acts higher up the endocrine pathway. Field studies indicate reliable fertility suppression without adverse behavioral effects during the breeding season when applied carefully. However, any handling or culling can still change behavior and should be monitored (Ransom et al., 2014). Recent research also shows how habitat and social conditions influence actual breeding (King et al., 2025) and how social instability may increase female aggression, altering breeding opportunities (Nunez & Adelman, 2025). In practice, select a mix of contraception that aligns with dart access and desired group stability. Monitor mare band-change rates and the number of effective breeding stallions while tracking genetic metrics to allow for adjustments (Nuñez et al., 2017; King et al., 2021).  

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Potential cross-boundary inflow (Fort McDowell Yavapai Nation; Salt River Pima–Maricopa Indian Community). 

Occasional inflows of 5–10% new horses from nearby areas can happen, but they are not guaranteed. These inflows should be considered opportunistic, natural genetic rescue—a benefit to plan for but not rely on. Even a small number of newcomers can reduce drift and refresh rare alleles if those animals breed (Waples et al., 2013; Nunney, 1993; Frankham et al., 2014a). When and if newcomers are identified, follow these steps:

(1)   Document immediately with photo IDs and microchips if possible. Collect fecal DNA within 24–48 hours (King et al., 2018; Hausknecht et al., 2010);

(2)   Let them breed by pausing contraception on immigrant mares for one or two seasons and avoiding removal of immigrant stallions while proceeding with other removals. This increases Nm and lowers Vk, raising Ne;

(3)   Maintain contraception on well-represented resident lines to favor under-represented (including immigrant) lineages in producing foals (Frankham et al., 2014a; Nuñez et al., 2017);

(4)   Follow regular biosecurity and welfare checks

(5)   Re-estimate your metrics (Ne, heterozygosity, allelic richness, ROH; plus mare band-switching if PZP is utilized) in the next cycle (King et al., 2018; King et al., 2021).

Because space use influences gene flow, maintaining corridors and functional band ranges increases the chances that rare movements will occur and impact genetic diversity (King et al., 2021; King et al., 2025). Use standard outbreeding-risk safeguards to minimise risks—such as the same species, similar environments, and evidence of historical connectivity (Frankham et al., 2011).  

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Action plan (measure → interpret → act → re-measure). 

• Baseline this year. Gather fecal genotypes from most animals (for N ~100–150, aim for ~80 unique individuals). Link genetic IDs to photo IDs or microchips (King et al., 2018). 

• Every 2 years. Sample 40–60 unique individuals. Report Ne, heterozygosity, allelic richness, and, when available, ROH. If PZP is widely used, also report mare band-change rates (King et al., 2018; Nuñez et al., 2009; King et al., 2021). 

• Triggers. Green: Ne ≥ 50 and diversity stable—continue rotations. Yellow: Ne 35–49 or 5–10% diversity decline over 4 years—widen male participation (rotate contraception off under-represented mares; keep it on standard lines) and revisit removals to avoid losing rare lines. Red: Ne < 35 or fast-rising ROH—treat natural inflow as first-line genetic rescue. If this is insufficient, plan a small, screened introduction (Frankham et al., 2014a, 2014b; Andersson et al., 2022; Hoban et al., 2021; Colpitts et al., 2022; Colpitts, 2024). 

• During required removals/adoptions. Avoid choices biased by genetics: use molecular relatedness and allelic value to retain individuals from rare lines and reduce male monopolies. Use PMx to minimize mean kinship when pedigrees exist. When they do not, kinship-based molecular rules generally outperform random selection (Lacy et al., 2011; Putnam & Ivy, 2014). 

• Context matters. Larger, less dense ranges support more bands and lower male monopolies. In contrast, closed systems, such as Sable Island, and small islands, like those in Greece, accumulate inbreeding more quickly and require earlier, more cautious genetic intervention (Plante et al., 2007; Colpitts et al., 2022; Katsoulakou et al., 2023).    

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References

Andersson, A., Karlsson, S., Ryman, N., & Laikre, L. (2022). Monitoring genetic diversity with new indicators applied to an alpine freshwater top predator. Molecular Ecology, 31(24), 6422–6439. https://doi.org/10.1111/mec.16710

Colpitts, J. (2024). Causes and consequences of variation in genomic diversity in Sable Island feral horses (Doctoral dissertation). University of Saskatchewan.

Colpitts, J., McLoughlin, P. D., & Poissant, J. (2022). Runs of homozygosity in Sable Island feral horses reveal the genomic consequences of inbreeding and divergence from domestic breeds. BMC Genomics, 23(1), 501. https://doi.org/10.1186/s12864-022-08729-9

Frankham, R., Ballou, J. D., Eldridge, M. D. B., Lacy, R. C., Ralls, K., Dudash, M. R., & Fenster, C. B. (2011). Predicting the probability of outbreeding depression. Conservation Biology, 25(3), 465–475. https://doi.org/10.1111/j.1523-1739.2011.01662.x

Frankham, R., Bradshaw, C. J. A., & Brook, B. W. (2014a). Genetics in conservation management: Revised recommendations for the 50/500 rules, Red List criteria and population viability analyses. Biological Conservation, 170, 56–63. https://doi.org/10.1016/j.biocon.2013.12.036

Frankham, R., Bradshaw, C. J. A., & Brook, B. W. (2014b). 50/500 rules need upward revision to 100/1000 – Response to Franklin et al. Biological Conservation, 176, 254–255. https://doi.org/10.1016/j.biocon.2014.05.006

Hausknecht, R., Bayerl, H., Gula, R., & Kuehn, R. (2010). Application of quantitative real-time PCR for noninvasive genetic monitoring. Journal of Wildlife Management, 74(8), 1904–1910. https://doi.org/10.2193/2009-421

Hoban, S., Bruford, M. W., Funk, W. C., Galbusera, P., Griffith, M. P., Grueber, C. E., Heuertz, M., Hunter, M. E., Hvilsom, C., Stroil, B. K., Kershaw, F., Khoury, C. K., Laikre, L., Lopes-Fernandes, M., MacDonald, A. J., Mergeay, J., Meek, M., Mittan, C., Mukassabi, T. A., O’Brien, D., … Vernesi, C. (2021). Global commitments to conserving and monitoring genetic diversity are now necessary and feasible. BioScience, 71(9), 964–976. https://doi.org/10.1093/biosci/biab054

Jones, M. M., & Nuñez, C. M. V. (2019). Decreased female fidelity alters male behavior in a feral horse population managed with immunocontraception. Applied Animal Behaviour Science, 214, 34–41. https://doi.org/10.1016/j.applanim.2019.03.005

Jones, M. M., Proops, L., & Nuñez, C. M. V. (2020). Rising up to the challenge of their rivals: Mare infidelity intensifies stallion response to playback of aggressive conspecific vocalizations. Applied Animal Behaviour Science, 225, 104949. https://doi.org/10.1016/j.applanim.2020.104949

Katsoulakou, E. M., Papachristou, D., Kostaras, N., Laliotis, G., Bizelis, I., Cothran, E. G., Juras, R., & Koutsouli, P. (2023). Genetic variability of small horse populations from Greek islands. Black Sea Journal of Agriculture, 6(2), 117–125. https://doi.org/10.47115/bsagriculture.1165045

King, S. R. B., Schoenecker, K. A., Fike, J. A., & Oyler-McCance, S. J. (2018). Long-term persistence of horse fecal DNA in the environment makes equids particularly good candidates for noninvasive sampling. Ecology and Evolution, 8(8), 4053–4064. https://doi.org/10.1002/ece3.3956

King, S. R. B., Schoenecker, K. A., Fike, J. A., & Oyler-McCance, S. J. (2021). Feral horse space use and genetic characteristics from fecal DNA. Journal of Wildlife Management, 85(6), 1074–1083. https://doi.org/10.1002/jwmg.21974

King, S. R. B., Cole, M. J., & Schoenecker, K. A. (2025). Horse affairs: Factors affecting reproductive success in a feral polygynous ungulate. Animal Behaviour, 227, 123281. https://doi.org/10.1016/j.anbehav.2025.123281

Lacy, R. C., Ballou, J. D., & Pollak, J. P. (2011). PMx: Software package for demographic and genetic analysis and management of pedigreed populations. Methods in Ecology and Evolution, 3(2), 433–437. https://doi.org/10.1111/j.2041-210X.2011.00148.x

Madosky, J. M., Rubenstein, D. I., Howard, J. J., & Stuska, S. (2010). The effects of immunocontraception on harem fidelity in a feral horse (Equus caballus) population. Applied Animal Behaviour Science, 128(1–4), 50–56. https://doi.org/10.1016/j.applanim.2010.09.013

Nunez, C. M. V., & Adelman, J. S. (2025). Mean mares? Habitat features influence female aggression in response to social instability in the feral horse (Equus caballus). Biology Letters, 21(1), 20240494. https://doi.org/10.1098/rsbl.2024.0494

Nuñez, C. M. V., Adelman, J. S., Mason, C., & Rubenstein, D. I. (2009). Immunocontraception decreases group fidelity in a feral horse population during the non-breeding season. Applied Animal Behaviour Science, 117(1–2), 74–83. https://doi.org/10.1016/j.applanim.2008.12.001

Nuñez, C. M., Adelman, J. S., & Rubenstein, D. I. (2010). Immunocontraception in wild horses (Equus caballus) extends reproductive cycling beyond the normal breeding season. PLOS ONE, 5(10), e13635. https://doi.org/10.1371/journal.pone.0013635

Nuñez, C. M. V., Adelman, J. S., Carr, H. A., Alvarez, C. M., & Rubenstein, D. I. (2017). Lingering effects of contraception management on feral mare (Equus caballus) fertility and social behavior. Conservation Physiology, 5(1), cox018. https://doi.org/10.1093/conphys/cox018 Nunney, L. (1993). The influence of mating system and overlapping generations on effective population size. Evolution, 47(5), 1329–1341. https://doi.org/10.1111/j.1558-5646.1993.tb02158.x

Plante, Y., Vega-Pla, J. L., Lucas, Z., Colling, D., de March, B., & Buchanan, F. (2007). Genetic diversity in a feral horse population from Sable Island, Canada. Journal of Heredity, 98(6), 594–602. https://doi.org/10.1093/jhered/esm064

Putnam, A. S., & Ivy, J. A. (2014). Kinship-based management strategies for captive breeding programs when pedigrees are unknown or uncertain. Journal of Heredity, 105(3), 303–311. https://doi.org/10.1093/jhered/est068

Ransom, J. I., Powers, J. G., Garbe, H. M., Oehler, M. W., Nett, T. M., & Baker, D. L. (2014). Behavior of feral horses in response to culling and GnRH immunocontraception. Applied Animal Behaviour Science, 157, 81–92. https://doi.org/10.1016/j.applanim.2014.05.002

Thompson MA, McCann BE, Rhen T, Simmons R. Population genomics provide insight into ancestral relationships and diversity of the feral horses of Theodore Roosevelt National Park. Ecol Evol. 2024 Apr 1;14(4):e11197. doi: 10.1002/ece3.11197. PMID: 38571790; PMCID: PMC10985374.

Waples, R. S., Luikart, G., Faulkner, J. R., & Tallmon, D. A. (2013). Simple life-history traits explain key effective population size ratios across diverse taxa. Proceedings of the Royal Society B, 280(1768), 20131339. https://doi.org/10.1098/rspb.2013.1339

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This response confronts the inaccurate statements about the validity of the Salt River Vegetation Assessment. As a researcher, scientist, and biostatistician, I know the report is completely valid. However, not everyone has my background, so I provided scholarly proof that this type of ASSESSMENT (note the lack of the words “research study‘) is completely valid and scientifically grounded. The University of Arizona is a prestigious institution that would not produce anything inferior or lacking in the proper scientific rigour, methodological standards, or ecological relevance expected of land-grant university research and Extension work. Below is the Rebuttal, as well as an academic review of the assessment below the rebuttal.

Certain advocacy groups are criticizing the University of Arizona’s April 2025 Vegetation Assessment of the Salt River Horse Management Area, with The Salt River Wild Horse Management Group topping the list. These groups dispute its legitimacy because it was not published in a peer-reviewed journal. The argument shows a fundamental failure to understand how ecological monitoring and land management operate within practical settings. Please read the first few paragraphs. Also of note, this is written by a non-scientist, with no formal research training most likely named Simone Van der Salm (Netherlands is not her real name). Ms Van der Salm is the president of the Salt River Wild Horse Management Group and ironically, Ms Van der Salm is NOT a United States citizen, perhaps why she is at ease disparaging the University of Arizona.

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Professional range scientists and ecologists at the University of Arizona Cooperative Extension developed the technical field report, which serves as the vegetation assessment. The assessment provides documentation of actual conditions while guiding resource management choices and delivering scientifically sound data to stakeholders, including land agencies, tribal partners, and the general public.

The scientific validity of management-grade research does not depend on peer review processes. The assessment’s quality is independent of peer-reviewed publication status because this format is unnecessary for credibility. Technical reports, field surveys, and unpublished data serve as the foundation for timely decision-making in all land and wildlife agencies and the Bureau of Land Management and the Forest Service. These include:

• Grazing permit evaluations
• Drought response actions
• Wildlife habitat management
• Emergency ecological assessments

This “gray literature” constitutes the primary source of information for both National Environmental Policy Act (NEPA) decision-making and agency decisions according to the National Environmental Policy Act (NEPA).

The validity of work depends on different factors other than peer-review status.
The success of applied ecological work depends more on methodological rigor than on peer-review status. Standard sampling protocols (dry-weight rank, comparative yield), transparent site selection methods, and data collection procedures should be used.

• Methodological rigor
• Appropriate interpretation grounded in ecological science.
• Clear documentation of findings.

The April 2025 assessment meets all of these criteria. The evaluation applied recognized vegetation assessment protocols, which combined dry-weight rank and comparative yield to document the distinct characteristics between grazing and non-grazing areas.

What motivations stem from these attacks against the assessment?
This report faces invalidation because of its non-journal publication status, but this argument lacks scientific-basis and demonstrates political dishonesty. This attack aims to deceive the public and decision-makers by equating essential research publications with field-based environmental assessments, although these represent entirely different approaches with separate objectives.

The application of this standard would render all the following assessments invalid:

• Most agency-led rangeland evaluation
• Federal wildlife habitat reports
• Environmental impact assessments
• Tribal ecological inventories
• Emergency drought response protocols.

No serious scientist or land manager would accept that logic.

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Continue reading →

Part Two: The Study (https://www.mdpi.com/2076-393X/12/1/96)

Abstract

This study evaluates the immunocontraceptive efficacy of Porcine Zona Pellucida (PZP) treatment on the Virginia Range free-roaming horse population, analysing the impacts of PZP on fertility rates over four years (2019-2022). Researchers monitored 2,817 mares, tracking vaccination records and resulting reproductive outcomes. The analysis demonstrated a significant reduction in foaling rates, suggesting a nearly 60% decrease in pregnancies due to the pzp treatment.

However, the study’s methodology faced criticism for lacking rigorous statistical analysis, insufficient control for confounding variables, and reliance on descriptive statistics without inferential modelling. Recommendations for future research emphasise the need for mixed-effects models and survival analysis to assess vaccine efficacy and duration of effect better, enhancing the overall robustness of findings in wild horse population management.

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Critical Scholarly Evaluation

Scientific Rigour and Methodology

• Strengths: Comprehensive field dataset; basic tracking of age, treatment, and reproductive outcomes.
• Major Weaknesses:
  • No statistical testing or modelling to establish causation.
  • No control group or comparison cohort.
  • No adjustment for confounders (e.g., predation, environmental change, mortality).
  • Relied on visual trends rather than hypothesis-driven methods.
• Verdict: Methodologically weak. Fails to meet the baseline standard for quantitative evaluation of treatment efficacy in population science.

 Statistical Soundness

• Strengths: Descriptive statistics are presented clearly.
• Major Weaknesses:
  • No inferential statistics, p-values, confidence intervals, or effect size estimates.
  • Despite rich longitudinal data, no regression, survival analysis, or repeated measures modelling.
  • Assumptions about vaccine efficacy duration were not tested against observed outcomes.
• Verdict: The study’s lack of statistical rigour is a serious flaw, as it makes population-level claims about contraceptive efficacy.

Journal Quality (MDPI Vaccines)

• Concerns:
  • Critics have criticised MDPI for operating with predatory tendencies, including fast-tracked peer review and questionable editorial practices.
  • While Vaccines is indexed and has an impact factor, it is not a top-tier veterinary science, ecology, or public health journal.
  • The article likely passed with minimal statistical peer scrutiny given its analytic weaknesses.
• Verdict: Publishing in MDPI Vaccines undermines the credibility of an already under-analysed dataset.

Overall Scholarly Contribution

• Comment: While the field data is valuable and the observational findings are intuitively aligned with known PZP effects, the analytical execution is too weak to support evidence-based decision-making. It reads more as a program summary than a rigorous scientific study.

Final Assessment

This study would not pass peer review in a journal with strict standards for statistical analysis or epidemiological rigour. It is unsuitable for drawing firm conclusions about efficacy, duration, or policy recommendations without reanalysis using appropriate statistical models.

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Detailed evaluation of Statistical Methodology in the PZP Wild Horse Study

Introduction

The “Immunocontraceptive Efficacy of Native Porcine Zona Pellucida (PZP) Treatment of Nevada’s Virginia Range Free-Roaming Horse Population” study (Vaccines 2024, 12, 96) evaluated PZP fertility control effects through darts on birth rates and demographic patterns of a significant wild horse population. Researchers monitored monthly records about each mare within the population across four years from 2019 through 2022. The researchers measured vaccination records, birth status, and other factors. The goal was to evaluate vaccine effectiveness by measuring annual birthrate changes and population dynamics as PZP treatment reached more horses. Our assessment focuses on the statistical techniques from this research study for their suitability alongside their reliability and bias control mechanisms, as well as their approach to modelling vaccine persistence and longitudinal data analysis to determine if alternative analytical strategies would have produced more substantial findings.

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Summary of Data and Statistical Methods Used

Data Structure: The study examined 2817 female horses across 48 months. Each mare had multiple monthly data points that recorded her study presence. Using different efficacy models, the authors recorded and computed several variables during each mare-month observation, including pre-2019 vaccination status and total pzp vaccination count. The authors monitored key variables, which included mare pregnancy status and conception status, alongside foaling status, age classification, and social group affiliations, and observed granuloma or abscesses at injection sites. The detailed longitudinal data at an individual level allowed researchers to analyse treatment delivery and reproductive results throughout the observation period.

The analysis employed vaccine efficacy scenarios to model the PZP duration of effect. The field longevity of PZP contraceptive effects remained unknown, so researchers established four vaccine duration scenarios that spanned from permanent effects to six-month, twelve-month, and eighteen-month efficacy. The researchers modified cumulative vaccination records for each mare by removing expired vaccinations based on selected time intervals. A mare’s vaccine efficacy period would expire six months after vaccination unless she received additional booster shots. The team calculated average vaccination numbers per mare through time for all specified scenarios. The program started by giving initial primers and boosters before conducting yearly boosters as needed. The average number of active vaccinations per mare reached approximately one by 2022 under the twelve-month efficacy assumption after an initial boost (Vaccines 2024, 12, 96). The study conducted a sensitivity analysis using these scenarios to show how changing vaccine persistence would impact both coverage measures and booster dosage requirements.

The study analysis focused primarily on descriptive analysis. The authors used database queries for basic count and summary statistics while employing R for exploratory data analysis and graphing. The researchers produced graphical representations of essential metrics, which included mean vaccinations per mare and proportion of mares foaling across different time points. The research results presented statistical data through proportion analysis, count methods, and time-series graphical displays while focusing on changes in the four years. They documented that vaccination coverage reached 72.5% of the herd during the fourth year in 2022, while foaling results underwent substantial alterations. The foaling rate calculated from the percentage of birthing mares revealed a consistent downward trend until 2022, when only 10% of mares became pregnant, while the pre-program percentage stood at approximately 33%. The foaling rate declined by approximately 58-60%, and pregnant mare numbers decreased to 10% of the total population, thus proving contraceptive effectiveness. Statistical significance tests or confidence intervals did not support the observed population associations because the results were presented without these statistical measures.

The research study omitted complex statistical model analysis to determine the vaccine effects on various outcomes while managing other influencing variables. The analysis results lack information about p-values, confidence intervals, and formal model coefficients. The authors show patterns (e.g. “foaling rates approximately halved in 2021 and by 2022 were further reduced by ~60%”) that result from higher vaccine coverage. Yet, they do not include logistic regression models to assess the probability of foaling against treatment variables. The entire study population acted as its time-based comparison in the observational and descriptive research methodology. The study presented visual results showing fertility metric reductions as vaccination programs expanded, instead of performing statistical hypothesis tests.

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Appropriateness and Rigour of the Statistical Methods

The study used descriptive statistical analysis to analyse a whole-population field program without an untreated control group. The study effectively linked PZP vaccine distribution with dramatic reductions in foaling statistics. The descriptive analysis method enables effective communication of raw results through examples, such as comparing the annual foaling rate of 1 in 3 mares before the program to 1 in 10 mares by 2022. The analysis gained strength from using monthly data examination and mare age class separation between mature and yearling groups to distinguish between juvenile non-breeders and adult breeders. The selection of descriptive statistics improved their accuracy by using the correct population of mature mares for foaling rate calculations.

The absence of inferential statistical tests or models restricts the analysis from correctly measuring uncertainty or establishing causality. The study implements PZP treatment changes to explain observed outcomes, but does not present any statistical evidence to support this assumption. The authors fail to demonstrate any statistical analysis which would have allowed them to determine how vaccinated mares compare to unvaccinated mares in terms of foaling rates after adjusting for age. The lack of statistical analysis creates uncertainty about certain aspects of the study. The absence of statistical modelling techniques prevents the assessment of year-to-year statistical significance and individual and subgroup response variability. The lack of error measurements (confidence intervals on the 58% foaling reduction or the 10% conception rate) prevents us from determining the extent to which natural variation or unreported variables contributed to the observed changes. The analysis shows associations, but fails to establish statistical causation or provide measures of treatment effect uncertainty.

The study results are population-level descriptions because the authors include all observed subjects in their analysis, freeing them from typical sampling errors. When considering the outcome as a complete census population, hypothesis testing becomes less significant. Scientists often employ modelling to generalise findings even in situations that could be classified as “census”. The study would have benefited from including inferential analysis to confirm that other factors did not influence the observed patterns. The study should have included a statistical model to account for the missing 2019 data and to check if the observed decrease from 2020 to 2022 surpassed the expected patterns by chance or natural trends. Without statistical evidence for “efficacy”, the study depends on assuming no other substantial changes occurred during the program period.

The observational nature of this study requires an explicit evaluation of the assumption that no other significant changes took place during the study period. A more rigorous approach would involve demonstrating that similar declines were not observed in control areas or untreated periods. The descriptive statistical methods offered essential data insights, yet failed to reach the necessary standards for drawing causal conclusions. The initial field report approach was suitable for the study, but it provides opportunities for additional analysis. The following section highlights the challenges related to confounding and bias that emerge from this study design.

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The treatment of confounders, assumptions, and biases in the study.

Researchers took several data handling steps to minimise possible biases in their study. The efficacy analysis was protected from confounding factors by excluding mares that lived in prohibited areas or received treatment in the last year. The analysis excluded horses living south of a highway because these horses missed one year of treatment opportunities, and this bias would distort results if they were included. The authors removed the mares from the Fernley area who participated in the program during the last year because their observation period was insufficient. The exclusion of these regions was appropriate because including primarily untreated areas in the analysis might have weakened the vaccine effect or introduced geographic factors as confounders. The authors removed these horses from the study because the core population received the treatment consistently. The study distinguished immature females from mature females to prevent foaling rates from being confounded by yearling population growth. The researchers implemented age-based stratification to guarantee that foaling rate data measured breeding-capable mare fertility while excluding young non-reproductive animals. Uncontrolled Confounders: Despite those steps, several potential confounders and biases remain unaddressed or only qualitatively acknowledged:

Time Trends and Environmental Factors: The study took place over 4 years, during which other changes could influence foaling rates. For example, forage availability can fluctuate, drought can occur, disease can spread, predation can be a significant problem, and all of these can influence reproduction and foal survival independent of pzp. The authors noted an excessively high foal mortality rate (up to 63% in 2022) due to predator presence. Such mortality doesn’t prevent conception, but could mean some foals were never documented (if predation occurred shortly after birth). No adjustment or sensitivity analysis was done to account for year-specific environmental factors. A more rigorous approach might include year effects in a model or compare to historic data on foaling in that herd under similar ecological conditions.

Population Structure Changes: The program itself likely altered the population structure (fewer foals born means the age distribution shifts older, and fewer mature mares may be added each year). The authors observed a decline in the absolute number of mature mares over time, which could partly be an outcome of contraception (fewer new females) but also could result from natural mortality or removals. In fact, by the end of 2022, 1,089 of the study mares were classified as “deceased” (natural attrition) and 22 were “removed” by management. If mortality was high (possibly related to drought or predation), the decrease in foaling could partly reflect fewer mares being alive or healthy enough to reproduce, not just contraceptive effects. The analysis did not control for the changing number of mares at risk each year beyond expressing foaling as a percentage. This is primarily acceptable (foaling rate inherently accounts for several mares). Still, if specific subsets of mares were more likely to die or be removed (for example, perhaps untreated mares ranging in risky areas suffered higher predation), that could bias comparisons. The study did not examine whether the mares that remained in the dataset differed from those lost, which is a potential source of bias

Pre-existing Fertility Differences: In any observational study, treated and untreated subjects might differ. Treatment rollout was widespread here, but not every mare was darted immediately. One potential confounder is individual mare fertility or social status. It’s conceivable that the easiest mares to dart (those often near people or water) might have different reproductive rates than those in remote areas (which possibly had lower baseline foaling or higher foal predation). Since treatment wasn’t randomised, any such differences could confound results. The study did not explicitly compare foaling rates in untreated versus treated mares in the same period – a comparison that could have been informative. Instead, all mares in treated areas were analysed as one group, with increasing overall treatment coverage. This means we assume no systematic differences between the first and last mares darted, which might not hold strictly. The authors did not address this possible bias.

Initial Conditions and Lag: The authors acknowledge a key temporal bias: many mares were already pregnant when vaccination started (April 2019, peak foaling season). Consequently, the first foaling season’s data do not reflect the vaccine’s impact (those foals were conceived pre-program), and even the second year included some foals from mares vaccinated mid-pregnancy. They correctly note that a lag in effect was expected until the second year. They handled this by interpreting 2019’s foaling rate as artificially low (due to undercounting) and focusing on drops after 2020. However, they did not formally adjust the analysis to exclude those initial pregnancies or control for whether a mare had already foaled when first treated. A more rigorous analysis could have, for example, excluded foals born in 2019 from the efficacy calculation or started the “clock” for each mare after her last pre-treatment foal to more cleanly measure new conceptions under treatment. The study’s efficacy metrics (58% reduction in foaling) were computed at the population level without such fine-tuning, which could slightly underestimate true efficacy due to that initial lag.T he authors took sufficient measures to eliminate apparent confounding effects (entirely untreated subpopulations) and acknowledged some biases (for example, underestimation of foals at the beginning). However, they did not use statistical controls for confounders in the analysis. The approach assigns all changes in foaling to the treatment, which, although likely true, is not definitively proven without either a control group or a multivariate analysis. The study would have been more methodologically rigorous if the authors had explicitly controlled for year effects, regional differences, or pre-treatment fertility in a model. A before-and-after comparison of the same mares or a subgroup analysis (e.g. horses that remained untreated longer) could have helped isolate the vaccine’s effect. There remains some (albeit small) possibility that factors other than PZP contributed to the observed outcomes.

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Modelling of Vaccine Efficacy and Assumptions about Longevity

One of the strengths of the study’s methodology was its attempt to bracket the unknown duration of pzp’s contraceptive effect by analysing multiple scenarios. The four efficacy-duration scenarios (permanent, eighteen months, twelve months, six months) adjusted cumulative vaccine counts per mare. This sensitivity analysis is commendable in recognising a key assumption – how long a single primer+booster prevents pregnancy – and showing how different assumptions would change the interpretation of how many “effective treatment units” each mare received. For example, by month 48, mares had 3.74 shots on average (no efficacy loss scenario), but under a 12-month efficacy assumption, this translated to maintaining roughly one active shot per mare (since older shots “expired”). The study found that under a 12-month efficacy model, the program reached a steady state of ~1 vaccination per mare per year after the second year, which the authors cite as a “robust recommendation for treatment frequency” (i.e. an annual booster). In practical terms, their data supports the idea that yearly boosters are sufficient to keep fertility low, aligning with the 12-month efficacy assumption.

The scenario-based modelling was helpful but relatively simplistic, and it assumes rather than infers the vaccine’s longevity. The study did not test which assumption was most consistent with the observed foaling data. Ideally, one could try to infer the duration of vaccine effect from the data, for instance, by examining if pregnancies occur around 12+ months after treatment without a booster. The paper did not report an analysis correlating time since vaccination with pregnancy risk. Instead, it effectively sidestepped that by presenting all scenarios. This is conservative (it doesn’t over-claim how long PZP works), but it means the study doesn’t pinpoint vaccine longevity. They lean on the 12-month scenario as a likely case, noting the system “reached stability” at annual boosting, which suggests the authors’ interpretation that ~12 months is close to the actual duration of strong efficacy.

A more rigorous approach could have used time-to-event modelling or regression to estimate vaccine efficacy decay. For example, a survival analysis could treat the “event” as a mare conceiving or foaling, and include a time-dependent covariate for whether the mare is within X months of a vaccination. This would allow an estimate of the hazard of conception returning as months since the last treatment increase. If the hazard jumps after 12 months, that would empirically support a 1-year duration. Alternatively, a logistic regression for each breeding season could include the number of months since last shot as a predictor of pregnancy, to see if efficacy significantly drops off at 12–18 months. The current study did not undertake these analyses. Instead, assuming several fixed durations provided a range of possible outcomes (from best case permanent to worst case 6-month). It showed that even in the worst case, the program still achieved a substantial fertility reduction (because boosters were given frequently enough). This addresses the question “how sensitive are our results to vaccine longevity?” but not “what is the vaccine longevity given our results?”The study determined modelling success by comparing foaling rate reduction and contraceptive achievement. They employed foaling rate as a surrogate measure for conception rate and an indicator of the efficacy’s opposite effect. The foaling rate directly measures pregnancy prevention because mares can foal only once yearly. The researchers directly confirmed that about 1/3 of mares used to become pregnant annually until the program started, but only 1/10 of mares became pregnant thereafter. According to the study data, the conception rate decreased by about 67%, which matches the reported 58–60% decrease in foaling numbers due to some initial underreporting. The vaccine effectiveness model operated on a basic binary system, which tracked whether mares were pregnant or not pregnant without assessing individual vaccine performance or foaling probabilities. Smaller controlled research studies demonstrate that a primer followed by a booster achieves 90% success in preventing foals during one year. The field study avoided publishing such statistics because mares received multiple vaccinations, and no untreated control group existed for comparison. The researchers based their program evaluation on a 58% reduction in foaling as the primary metric.

The study presented vaccine efficacy assumptions with clear transparency through basic methods. The model presented realistic scenarios instead of performing a statistical analysis to determine how the vaccine effectiveness changed over time. Future research should adopt models that analyse the data to match efficacy-decay patterns. The evaluation could use mixed-effects models with random mare effects to examine the relationship between foaling rates and months since vaccination. The authors must calculate “time since last vaccination” to understand how fertility probability evolves post-treatment. The research would deliver data-based results about how long PZP maintains its effectiveness within this population. Such modelling could become more accurate by adding previously established individual vaccine efficacy from prior studies (e.g., two initial doses produce about 90% contraceptive success in the first year ) as prior knowledge or constraints. The study proves that annual booster injections are necessary for operations, but fails to deliver a precise analysis of vaccine duration, which presents an opportunity to expand research.

The data collection structure allows for advanced statistical analysis by implementing longitudinal data structures. The research contains natural longitudinal data because scientists monitored the same mares for four consecutive years. Advanced statistical models, including mixed-effects models, GEE, and time-series methods, can be applied to utilise repeated measures while accounting for individual differences in the data. The study authors omitted employing advanced statistical models in their published work, and this paper evaluates their decision.

The analysis presented a simplified view by combining monthly observations into yearly summaries and skipped explicit modelling of mare-specific patterns, thus treating each monthly observation as an independent event. The reproductive outcomes between consecutive years for the same mare remain correlated because mares treated consistently have higher probabilities of not foaling in 2021 and 2022. Statistical tests performed on this data without repeated measures consideration would result in incorrect uncertainty calculations because 2817×48 mare-month points cannot be treated as entirely independent data points. The authors safely avoided this error by refraining from carrying out statistical tests on these data points. The longitudinal data served to calculate population-level metrics, including monthly foaling rates, which automatically combined information from different mares. The problem of pseudo-replication remains negligible because their analysis focuses on descriptive statistics rather than statistical tests.

Not implementing a mixed-effects model prevented the study from quantifying mare and band-specific variability. Some horses would fail to become pregnant regardless of treatment, while others might achieve pregnancy despite repeated vaccinations, possibly due to vaccine resistance. A mixed-effects logistic regression model that includes mare-specific random intercepts could measure fertility baseline variations between mares. The analysis would determine vaccine effectiveness by considering these individual differences in the data. The analysis would determine intra-mare correlation (how consistent mare status remains) and residual variance levels. The current study presents an aggregate view that might hide significant patterns regarding the birth of a few foals from specific “problem” mares who either missed booster shots or failed to react immunologically. A more complex modelling approach would provide the solution to this question.

Time-series approaches analyse the herd as one unit to study monthly foal counts and their corresponding rates. The monthly data shows clear seasonal patterns because horses reproduce seasonally, with an overall decrease in the number. A time-series model would use ARIMA with seasonal components or state-space models to measure the trend structure and verify its statistical importance against the observed count variability. An interrupted time series analysis would evaluate April 2019 as the beginning of treatment to determine if there were any significant changes to foaling rate slopes or levels post-intervention when compared to pre-treatment periods. The main obstacle in this situation is the limited pre-intervention data from 2019 because they only had several months before vaccination, which were also partially missing. Analysing historical foal counts from 2018 would enable researchers to determine seasonal patterns, which could be compared to the observed changes in 2019–2022. The study authors did not apply this method since they followed the breeding season timing, which stayed normal, while the foaling peak decreased in amplitude. Through seasonal time-series decomposition and seasonal ARIMA models, researchers could have provided more substantial evidence that the peak timing and duration did not shift along with the confirmed decline in foaling rates. The authors use graphs to demonstrate these findings, yet models provide a statistical backing for their claims.

The data structure (many mares, each observed many times) suggests that a generalised linear mixed model (GLMM) is suitable for analysis. For example, one could set up a logistic GLMM where the outcome is whether a mare foals in a given year (or conceives in a given season) with fixed effects for treatment metrics (e.g., number of shots received, or a binary treated vs not in that year) and random effects for mare and perhaps for year or herd area. Such a model could directly estimate the impact of treatment on the odds of foaling. It could answer questions like: How much does each additional vaccine dose reduce the odds of a mare foaling if adequately specified? Or what is the odds ratio of foaling for a treated mare versus an untreated mare? – controlling for other factors. This would transform the largely qualitative efficacy claim into a quantitative one. For instance, other researchers have used logistic regression to estimate contraceptive effects: Roelle et al. (2017) modelled the probability of foaling in treated vs. control groups using logistic regression and reported that treated mares had dramatically lower odds of foaling (with odds ratios and p-values to demonstrate significance). Adopting a similar approach here would allow the authors to state something like “PZP treatment was associated with an X-fold reduction in the odds of foaling (p < 0.001)”, which is far more statistical language. The current study instead uses phrasing like “associated with a 58% reduction in foaling” without statistical inference, so incorporating a GLMM would tighten that causally.

Another advantage of mixed models is handling group-level effects. The Virginia Range is large, and the data included different herd areas and bands (which were recorded ). There may be random effects of band (harem) – for example, differences in stallion behaviour or band terrain could influence foal outcomes. A hierarchical model could include a random effect for band or geographic area, accounting for clustered outcomes. This was not explored; all data were pooled. While these random effects might not dramatically change the main conclusions, their inclusion would improve the precision of estimates and allow checking that results are not driven by, say, one particular sub-area.

Use of Repeated Measures for Precision: The study forgoes some statistical power by not using the longitudinal nature in an inferential model. Each mare’s history provides multiple data points that, if modelled appropriately, could strengthen confidence in the effect. For example, if a mare serves as her control (pre- vs post-treatment), that self-comparison can account for individual fertility level and improve detection of treatment effect. A before-and-after paired analysis could have been done for mares that had known fertility before PZP and after. The authors did not explicitly conduct such a paired analysis. Still, one could imagine using the data in that way (e.g., “Of mares that had foals before treatment, 80% had no foal after treatment” – a statement that would be compelling evidence of efficacy). Instead, they looked at aggregate foaling rates year by year.

The data structure was rich, but the analysis did not exploit it with advanced models. Mixed-effects models or GEE (generalised estimating equations) would account for the repeated measures and provide more robust inference (especially if one wanted to generalise to other herds or future years). Time-series models could better characterise the trend and seasonal patterns, offering formal tests for pattern changes. The absence of these models is a limitation in the study’s methodology, not in terms of their making a mistake, but in terms of missed opportunities for more profound insight. The results as presented are credible, but a reader might wonder if a rigorous model would yield the same conclusions (most likely yes, but it should be demonstrated). Employing such models would enhance confidence that the observed decline in foaling is genuinely due to the vaccinations and not an artefact of unmodeled variability or correlation.

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Optimality of Chosen Methods vs. Alternatives

The descriptive longitudinal approach effectively showed a large-scale effect but failed to deliver causal or precise results. We detail how alternative statistical methods would lead to a more robust or informative analysis:

Applying Generalised Linear Models (GLMS) through logistic or Poisson regression models would enable researchers to incorporate treatment as a predictor variable while performing formal tests of its effect. The research design uses a logistic GLM to analyse annual foaling success (yes/no per mare-year) alongside variables like the number of vaccine shots administered during that year, mare age, and geographic location. The analysis produces an evaluation of vaccine effectiveness per dose administered. A Poisson or negative binomial GLM could model the count of foals per mare (mostly 0 or 1, but could handle a mare having zero foals vs one foal over the period, etc.). Due to the large number of zero values, the logistic approach presents the most straightforward solution. Implementing a GLM analysis would boost the research methodology by providing p-values and confidence intervals for evaluating the main effect of interest. The research methodology enables users to verify interaction effects and non-linear relationships between variables (for example, the decreasing value of applying more than two shots). The original study did not implement these statistical procedures; thus, a GLM would represent a more robust method to establish cause-effect relationships and measure effect sizes.

 Mixed-Effects Models: The discussion highlighted that a GLMM (mixed model) would represent an even better solution because it handles repeated measurements and hierarchical data structure. The most suitable method for handling this data type would be mixed models. It could process time-dependent variables (such as total vaccine injections received by each mare) and include random mare intercepts. The outcome would deliver a vaccine effect measurement that applies to the entire population while providing an uncertainty measurement. A mixed model analysis might demonstrate that controlling for mare variations and yearly effects reveals X% decreased odds of foaling with each additional vaccination, precise confidence intervals, and Y times increased likelihood of foaling among untreated mares. The study would validate the efficacy statements through statistical methods. A mixed model would help analyse the predictors of treatment failures by examining whether the failure rates correlate with missing booster shots or being located in certain areas. The detailed information about the population becomes difficult to access through a basic descriptive summary.

Survival Analysis: Survival analysis or time-to-event methods could determine the duration of fertility suppression caused by treatment. The analysis of time to first foaling can start from the program’s initiation by treating death or removal as censoring events for mares who begin the program. The survival curve for “time to foaling” would extend further to the right (foaling time becomes longer) when pzp proves effective compared to a curve without treatment. The remaining untreated mares who unintentionally did not receive treatment could serve as a survival analysis comparison group. A survival model that includes time-dependent covariates enables researchers to analyse the exact moment when a mare received vaccination to examine the immediate change in her foaling hazard. The method provides exceptional power to evaluate waning vaccine efficacy since researchers can assess whether the foaling hazard elevates after twelve months post-vaccination. The current study lacks survival analysis, which would provide detailed information about the duration of vaccine effectiveness.

This study failed to apply these methods. Using these methods would increase confidence that the pZP vaccine caused the decline (over simply being correlated with it).

Modelling Heterogeneity and Uncertainty: Other methods could also increase the understanding of the heterogeneity of efficacy. For example, perhaps older mares are slightly more or less responsive to the vaccine; a model could examine whether there is an interaction between age and treatment. Or possibly efficacy increases after a mare has had multiple boosters (immune response builds) – a longitudinal model could assess whether fertility rates dropped further for mares that received boosters in consecutive years compared to those with gaps. The descriptive analysis provided suggestions (e.g., one year’s treatment is enough). Still, a model could support that by showing, for example, that mares who missed a year were significantly more likely to foal, thus demonstrating the importance of not exceeding a twelve-month gap. This is important, as we would like to know how much trust we have in, for example, “10% conception rate” rather than 10% ± 5%. With thousands of data points, uncertainty is likely small, but it should be stated.

Was the chosen method optimal? From a purist statistical standpoint, no, the methods were not optimal for inference. They were sufficient for description and probably sufficient to convince readers qualitatively (because the effect is significant), but they do not meet the highest standards of analytical rigour. The optimal methodology would likely be a combination of the above alternatives: perhaps a mixed-effects logistic regression for foaling outcomes (to estimate effect size and control confounders), complemented by a survival analysis for duration of efficacy, and possibly a time-series analysis to confirm no extraneous trend shifts. These methods would give a comprehensive, robust picture: that the vaccine works, how strongly it suppresses fertility, how long it lasts, and that the observed decline is due to the intervention and not other factors.

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Critiques and Recommendations for Improved Rigour

Critique Summary: The study was very effective in showing that a pzp darting program can cause a sharp decline in foal production in a wild horse population, but the statistical analysis was heavily based on observation of trends without much formal modelling. Since there is no inferential statistics, the results, although persuasive, are based on the assumption that no other factors could explain the changes. Potential confounders (environment, mortality, heterogeneous treatment application) were not fully controlled, and the powerful longitudinal nature of the data was underutilised. The approach to vaccine efficacy (using predefined duration scenarios) was informative but did not extract the maximum insight that a data-driven model could provide. In essence, the analysis provided evidence of efficacy but did not provide measurement of efficacy with estimates of precision or tests of significance.

Recommendations: To enhance methodological rigour in this and similar studies, we recommend the following:

Incorporate a Control or Comparison: If an outright control group (untreated horses) is not ethically or logistically feasible, use internal comparisons. This could include untreated periods or regions as quasi-controls (with appropriate caveats), or comparing mares before vs. after they receive treatment (within-subject comparison). Even data from the fringes of the study (e.g., the excluded areas) could be leveraged via causal inference techniques to strengthen the argument that the observed declines are due to PZP and not an overall herd phenomenon. For example, a difference-in-differences analysis using the south-of-Highway-50 horses as a reference group could control for year effects on foaling rates.

Use Generalised Linear Mixed Models: Re-analysing the data with a GLMM would likely be the most informative improvement. A mixed model could solve many of the abovementioned problems: it can control for confounders (including covariates such as year or age), handle the repeated measures (random effects for mares), and estimate the treatment effect with a significance test. It would provide outputs like an odds ratio for vaccination effect, which could be directly compared to other studies or used in meta-analyses. Such a model could also implicitly include the vaccine efficacy duration: e.g., include terms for whether a mare is within 0–6 months post-shot, 6–12 months, etc., to see where fertility increases. We highly recommend that the authors or future researchers perform a mixed model analysis to quantify PZP’s effect on individual fertility risk.

Conduct Survival Analysis for Efficacy Longevity: A focused survival or time-to-foaling analysis should be done to estimate how long the vaccine protects a mare, in addition to the above. Mares should be tracked from their last treatment to see when (if at all) they will produce a foal next. A Kaplan-Meier curve would visually show the proportion of treated mares remaining foal-free over time, and a Cox proportional hazards model could test differences between groups (for example, mares that received boosters versus only primers) or estimate the hazard increase as time since treatment increases. This would test the “6, 12, 18 months vs permanent” assumptions and likely pinpoint a more precise duration (for example, perhaps finding that pregnancy hazard starts rising after 12–16 months). It also naturally handles censoring (mares that die or are removed). The survival analysis results could then be translated into an estimated efficacy period with confidence intervals (for example, “PZP effectively prevented foaling for a median duration of X months in treated mares”).

Address Biases in Data Collection: The study noted incomplete foal documentation in the first year and high foal mortality later. Future analyses should consider adjusting for detection bias, perhaps using auxiliary data like known predator kills or pregnancy observations. If foals are being missed, one might incorporate a correction factor or at least do a sensitivity analysis (e.g., “if X unseen foals existed, would it change conclusions?”). Also, explicitly incorporate initial pregnancy status: a suggestion is to start the analysis of foaling rates from mid-2020 onward (once no mare is still carrying a pre-treatment pregnancy) to isolate the treatment effect. Alternatively, include a covariate for whether a mare foaled in 2019 (meaning she wasn’t prevented that year) when modelling 2020 outcomes, etc. This could control for differences between mares that were initially pregnant vs not.

Use Multi-Variable Models to Adjust for Confounders: Even a straightforward multivariable logistic regression (not necessarily mixed if one does per-year analysis) could include year (or environmental indices) to adjust for annual conditions, age of mare (fertility can decline in very old mares, and very young mares have lower fertility; the study assumed >1 year as equal, but a 2-year-old vs a 15-year-old might differ), and location or band as covariates. By doing so, one can say “controlling for year and age, treated mares had an X% lower probability of foaling.” This increases confidence that the effect isn’t due to those other factors. It appears the authors recorded variables like band and herd area, so using them in a model to account for spatial clustering or stallion effects would be feasible and advisable.

Provide Uncertainty Estimates: Wherever possible, future reports should include confidence intervals or similar measures for key outcomes. For example, “58% reduction” could be accompanied by a 95% confidence interval (even if derived from a model or a data bootstrap). This communicates the statistical certainty. Given the large sample, the intervals might be narrow, but reporting them is good practice. Likewise, the “10% conception rate” could be given as 10% ± some margin. This would formally indicate how much variation in these percentages could occur due to randomness (though here randomness is mostly from which mares were observed or missed, since it’s population-level).

Explore Alternative Outcome Metrics: The study focused on foaling and conception rates. Another complementary metric is population growth rate. By combining foaling rates with mortality rates, one can estimate the annual population growth and see how it has changed. The authors mention zero-population-growth targets and that other studies took years to see a decline. A population projection model (even a simple exponential or matrix model) could be used to estimate the growth rate with and without the observed fertility control. This would translate the findings into a more aggregate outcome (herd growth slowed from x% to y% per year). It’s not purely a statistical method, but rather a modelling exercise that could strengthen the argument that pzp moved the herd from a growing state to a near-stable state. Coupling such a model with uncertainty from the data (via simulation) would further enhance the rigour. The research study employed basic methodologies, which produced easy-to-understand descriptive results, although more complex statistical methods should be used to verify and expand these findings. The analysis would gain strength through applying GLMMS for treatment effect and survival analysis for duration, alongside causal inference for unbiased effect estimation. The research methods would demonstrate the same conclusion regarding pzp darting effectiveness in lowering wild horse birth rates, but with strengthened evidence from statistical significance, controlled comparisons, quantitative effect size, and longevity measurements. The application of rigorous methods is vital because it enhances scientific precision and helps decision-makers rely on exact numbers (e.g., “The vaccine will reduce foaling probability by at least X% for up to Y months at a 95% confidence level”).

Future analyses of this dataset or similar field studies should utilise mixed-effects logistic models to estimate efficacy while accounting for repeated measures, apply survival analysis to determine how long the contraceptive effect lasts per treatment, carefully control for confounding factors either by design or statistical adjustment, and include measures of statistical uncertainty. This study’s excellent large-scale field effort will achieve equal robustness in statistical evidence through proper analysis, thus establishing its findings and guiding best practices for wild horse population management with enhanced accuracy.

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Summary of the Paper

The study investigates the immunocontraceptive efficacy of Porcine Zona Pellucida (pzp) treatment on the free-roaming horse population in the Virginia Range. Over four years (2019-2022), researchers monitored 2,817 mares, tracking their vaccination records and reproductive outcomes. The results indicated a significant reduction in foaling rates, suggesting a nearly 60% decrease in pregnancies attributable to the PZP treatment. However, criticisms arose regarding the study’s methodology, which lacked rigorous statistical analysis and adequate control for confounding variables. Recommendations for future research highlighted the necessity of employing mixed-effects models and survival analysis to improve the robustness of findings related to vaccine efficacy and its duration of effect.

Recommendations for Future Research

1. Engage Statistical Experts: Collaborate with a statistician with experience analysing ecological data, particularly involving wild horse populations. Their expertise can enhance the rigour of the statistical methods used.
2. Local Research Collaboration: Involve researchers who are geographically closer to the Virginia Range horses. This can provide valuable insights into local environmental factors and horse behaviour that may influence reproductive outcomes.
3. Mixed-Effects Models: To analyse the data, use mixed-effects models. This approach can account for individual mare variation and repeated measures, providing a clearer understanding of the treatment effects.
4. Survival Analysis: Conduct survival analysis to accurately assess the duration of PZP contraceptive effects on the mare population. This method can help determine how long the vaccine remains effective post-treatment.
5. Control for Environmental Variables: Incorporate environmental factors such as forage availability and predator presence into the analysis to control for confounding influences on foaling rates.
6. Longitudinal Tracking and Comparison: Implement a longitudinal design that allows for before-and-after comparisons within the same mares, offering more precise insights into the treatment’s effects over time.
7. Community Engagement: To ensure the research aligns with community goals and conservation efforts, foster relationships with local stakeholders and horse management organisations.

By enhancing statistical rigour and incorporating localised expertise, future research can produce more reliable findings that support effective wild horse population management strategies.

Dr. Meredith Hudes-Lowder
Biostatistician
©April 2025