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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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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

Some background history

White markings in horses serve no biologic purpose, nor are they an adaptation to avoid predation or increase survivorship. On the contrary, white markings on a horse can make it easier for predators to detect them due to the way light interacts with different colors and how animals perceive contrast in their environment. Dark colors, such as black and brown, tend to absorb more light and blend more seamlessly into natural surroundings, whereas white reflects more light, making it more conspicuous, especially in low-light conditions or against darker backgrounds (Caro, 2005). This increased visibility can make a horse with white markings stand out from the rest of the herd, drawing the attention of predators.

Additionally, many predators rely on motion detection and contrast sensitivity rather than color perception when hunting. Studies on animal vision suggest that predators like wolves and big cats have dichromatic vision, meaning they perceive colors differently than humans, primarily distinguishing between blue and yellow wavelengths while having difficulty differentiating reds and greens (Jacobs, 1993). Because white markings create a stark contrast against darker coat colors or natural surroundings, they can enhance the visibility of movement, making a horse’s motion more detectable to a predator’s keen eyesight.

In some environments, particularly in wooded or shadowed areas, a horse with large white patches may stand out more distinctly compared to a uniformly dark-colored horse, which benefits from more effective camouflage (Caro, 2016). This is particularly relevant in species where natural selection has favored solid or muted coat colors in wild equines, such as zebras, which use disruptive coloration to confuse predators (Ruxton, Sherratt, & Speed, 2004).

While white markings may increase visibility in certain conditions, their impact varies based on habitat and lighting. In bright, open environments, such as snowy regions or sandy plains, white markings may offer some blending advantages. However, in forests, grasslands, or dusk and dawn lighting conditions—when many predators are most active—white markings can provide a disadvantage by increasing a horse’s contrast against the environment, making it easier for predators to spot and track them.

Why White Markings are Preferred

Humans selectively bred horses for white facial and leg markings due to a combination of aesthetic, practical, and genetic factors. These markings were often associated with visibility, tradition, and cultural significance, as well as linked to specific genetic patterns.

1. Increased Visibility

One of the primary reasons for selecting horses with white markings, especially on the face and legs, was to enhance their visibility in low-light conditions. White facial blazes and leg stockings made it easier to identify individual horses, particularly in herds or during nighttime activities (Gower, 1999). This was especially beneficial for cavalry, working horses, and herding livestock in dimly lit environments.

2. Aesthetic and Cultural Preferences

Throughout history, different cultures favored distinct coat patterns. In many European and Middle Eastern traditions, white markings were seen as noble and desirable. Medieval knights often preferred horses with white blazes and stockings because they were considered more striking and prestigious (Bennett, 1998). Similarly, in North America, flashy white markings became popular in breeds such as the American Paint Horse and Pinto horses.

3. Association with Temperament

Some studies suggest a correlation between coat color, white markings, and temperament. While anecdotal, many horse breeders believed that certain markings were linked to docility or alertness (Haase et al., 2021). This perception may have led to selective breeding for specific markings as a means of predicting and influencing behavior.

4. Genetic Linkages and Breed Standards

White facial and leg markings are largely controlled by genes affecting pigmentation, particularly the KIT and MITFgenes (Brooks et al., 2007). As breeding programs developed, horses with desirable traits—including markings—were preferentially selected. Some breed registries, such as the American Quarter Horse Association (AQHA), permit or even favor white markings within certain limits, reinforcing their propagation through generations (AQHA, 2023).

5. Cultural and Superstitious Beliefs

Certain markings have been historically linked to superstitions. For example, the “badger face” marking (inverted white blaze) was considered lucky in some cultures, while others believed extensive white on the face (e.g., a bald face) could indicate poor vision or sensitivity to sunlight (Sponenberg, 2009). These beliefs influenced breeding choices in different regions.

Humans bred horses to have white facial and leg markings primarily for their visibility, aesthetic appeal, perceived temperament traits, and genetic linkages. Over time, these preferences became ingrained in breed standards and cultural traditions, leading to the continued propagation of these markings in many modern horse breeds.

White Markings in Equine Ancestry

The absence of white markings in wild horses, such as the Przewalski’s horse, suggests that mustangs in America are not descended from native horses can be supported by several points drawn from the provided references.

1. Lack of White Markings in Wild Horses: Przewalski’s horse, the last truly wild horse species, lacks white markings, indicating that such traits are not inherent to wild equine populations. As stated, “the markings were not present, or were very rare, in the primeval wild horse, as evidenced by their absence in Przewalski’s horse” (Woolf, 1991, p. 3). This suggests that white markings are a trait associated with domestication rather than a natural occurrence in wild equines.
2. Selective Breeding and Aesthetic Preferences: Domestic horses have been selectively bred for various traits, including coat color and markings, primarily for aesthetic purposes. The document mentions that “white markings are assumed to be flashy, highlighting the limb action which is particularly important in shows” (Stachurska & Ussing, 2012, p. 75). This selective breeding has led to the proliferation of white markings in domesticated horse breeds, which are not found in their wild counterparts.
3. Genetic Evidence of Selective Breeding: The presence of white markings in domestic horses is associated with several genetic loci that are not prevalent in wild populations. The genetic basis for white markings is multifactorial and involves complex inheritance patterns, as discussed in multiple studies that highlight the heritability and genetic underpinnings of these traits (Woolf, 1990, p. 250). In contrast, wild horse populations like the Przewalski’s horse show a different genetic makeup, lacking the alleles responsible for white markings.
4. Implications for Mustang Ancestry: If mustangs were directly descended from native horses in the Americas, one would expect to find similar genetic traits, including white markings. However, the absence of such markings in their wild ancestors suggests that mustangs likely descended from domesticated horses, which were selectively bred for these traits in Europe and brought to the Americas by European settlers. The genetic diversity and presence of white markings in mustangs can thus be attributed to the introduction of domestic horses with these traits rather than a lineage from native equines.

In conclusion, the lack of white markings in wild horses like the Przewalski’s horse supports the notion that mustangs, as domesticated descendants, inherited these traits through selective breeding practices focused on aesthetics, rather than through lineage from native American horses. This argument is reinforced by genetic evidence indicating that the selective pressures for coat color and markings are a product of domestication rather than a natural evolution in wild horse populations. There are other reasons we know mustangs are descended from domestic horses, but that a topic we will touch upon later in this post.

Inheritance of White Markings

The white facial and leg markings on horses are described as multifactorial, meaning that the genes are not the only factor that determines their inheritance. It has been found that these markings are controlled by several genetic loci and at least some of the genes involved include MC1R, KIT and MITF. 

1. Multifactorial Inheritance: Some white facial and leg markings are not inherited in a simple Mendelian fashion but rather by a polygenic or multifactorial inheritance system where many genes interact to produce the phenotype. This pattern of inheritance suggests that the frequency of white markings can vary greatly between individuals because of the additive effects of several alleles (Woolf, 1990; Rieder et al., 2008). 

2. Key Genes Involved: Melanocortin 1 Receptor gene (MC1R): Mutation of this gene product causes changes in coat color and plays an important role in the extent of white markings especially in chestnut horses where they have more white markings than other colors (Woolf, 1990; Haase et al., 2013). KIT: The KIT gene is also responsible for the development of the white marking and the size of the area with white marking. Many alleles at this locus have been found to be associated with various forms of white spotting including dominant white (Haase et al., 2013; Rieder et al., 2008). MITF (Microphthalmia-associated Transcription Factor): This gene has been found to be responsible for certain white spotting phenotypes including the splashed white phenotype (Hauswirth et al., 2012). 

3. Genetic Correlation: Studies have shown that heritability for white markings is high, suggesting genetic basis. For instance, heritability estimates for facial markings can be 0.69, and total leg markings can be 0.68, which indicates that genetics is a significant determinant of these traits (Woolf, 1990; Rieder et al., 2008). 

4. Environmental Factors: In addition to genetic influences, environmental factors and stochastic (random) events can also affect the development and manifestation of white markings. This includes the possibility of developmental noise, which are stochastic variations that occur during embryonic development that may result in the variation in the migration and survival of melanoblasts (Mintz, 1974; Woolf, 1995). 

5. Complex Interactions: The interaction between the basic coat color (which is determined by MC1R and Agouti loci) and white markings is also rather complicated. For example, chestnut horses (e/e) are typically more heavily marked than bay (E/ –) and black (a/a; E/ –) horses, which shows the role of other genetic factors (Woolf, 1991). 

The Next Generation

The inheritance of white facial and leg markings in horses is a complex trait controlled by several genetic loci and environmental factors with significant contributions from MC1R, KIT, and MITF genes and their products, which result in various phenotypic outcomes. The heritability of facial and leg markings is fairly high, with the heritability estimates for facial markings being estimated at 0.69 and for limb markings at 0.68, and the overall heritability for both facial and limb scores can be up to 0.77 (Woolf, 1990; Stachurska and Ussing, 2012). These markings are caused by several loci, and the two most important genes have been identified as MC1R and KIT that are involved in pigmentation. The MC1R gene codes for the receptor that regulates type of melanin produced while the KIT gene is associated with white spotting patterns (Rieder et al., 2008; Haase et al.,2013). Furthermore, it has been postulated that the expression of white markings is not only dependent on genetic factors but also on stochastic events that take place during embryonic development that affect melanoblast survival and migration (Woolf, 1990). This complexity suggests that other factors, including environmental factors, may also contribute to the variation in phenotypic expression of white markings in different individuals (Woolf, 1995; Rieder et al., 2008).

White markings can be inherited from parents to offspring. White markings in horses are known to be influenced by genetic factors, especially the mutation of certain alleles at certain loci. For example, a study showed that if both parents have white facial markings, the offspring are likely to have these markings and 61.8% of such offspring will have white markings (Document: Encina et al. – 2024). On the other hand, if both parents are without white markings, then a smaller percentage of the offspring (22.6%) have these markings.

Size matters?

The size of white markings on legs in horses is dependent on genetic and non-genetic factors. Key points include:

1. Genetic Factors: The heritability of white markings is quite high, and the studies have shown that the heritability values are between 0.68 and 0.77 for different parts of the body (Woolf, 1990; Rieder et al., 2008). The MC1R, KIT, and MITF genes have been associated with the frequency of white markings. For instance, the mutation of the recessive allele of the MC1R gene, which is responsible for the chestnut coat color, has been associated with more extensive white markings (Negro et al., 2017; Haase et al., 2013).

2. Interaction of Genes: There are interactions between different genetic loci such as MC1R and KIT, where the genetic control of the quantity of white marking depends on the base coat color of the horse (Patterson Rosa et al., 2022; Cuffe, 2024).

3. Environmental and Stochastic Factors: Other factors, which can be regarded as environmental variables, affecting the intrauterine development of the horse, can also influence the expression of the phenotype of white markings. Stochastic events that affect the survival, movement and mitosis of melanoblasts (the precursor of melanocytes) contribute to the variation in white markings (Woolf, 1995; Stachurska and Ussing, 2012).

4. Sex and Coat Color: It has been found that males have slightly more white markings than females and the base coat color greatly affects the frequency of white markings with chestnut horses having more extensive markings than bay or black horses (Woolf, 1990; Rieder et al., 2008). In summary, the quantity of white in leg markings is controlled by genetic inheritance, particular gene interactions, environmental factors and stochastic events that occur during development.

The Base Coat

Also, different coat colors have different effects on the chances of white markings in offspring. For example, chestnut horses are more likely to have white markings than black horses (Document: Encina et al. – 2024). In general, although genetics is an important factor in the expression of white markings, other factors like environmental conditions and developmental processes also play a role in the expression of these markings. This indicates that the above mentioned genes are involved in the determination of the shape of white markings, such as a star or a blaze.

For example, chestnut horses, which have a specific genotype (e/e), have more extensive white markings than bay or black horses and the amount of white marking is generally higher on these horses than on other horses (Woolf, 1990; Rieder et al., 2008; Haase et al., 2013). Furthermore, the heritability of markings suggests that the extent and perhaps the shape of these markings can be passed down from generation to generation and that certain genetic combinations are more likely to result in more pronounced markings (Woolf, 1990; Rieder et al., 2008). Therefore, even though there are stochastic events that can lead to the final appearance, the genetic predisposition to certain shapes of stars and blazes is indeed inherited. Specific coat patterns are associated with specific facial markings?

Horse coat patterns are accompanied by specific facial markings due to genetic control of pigmentation. Here are some notable associations:

1. Tobiano Pattern: Tobiano horses are homozygous for white and have large patches of white that cross the spine and have face marks that are usually star or stripe like. This is associated with the KIT gene which is involved in white spotting phenotypes (Haase et al., 2013).

2. Sabino Pattern: This pattern has a patchy white marking on the face and other parts of the body and is similar to the leopard complex. The KIT gene is also involved here, that is through mutations that result in different levels of white marking including facial patches (McFadden et al., 2024).

3. Splashed White: Horses with this type of splashed white pattern have a lot of white on the face including up to a full blaze or completely white face. This phenotype is caused by mutations in MITF and PAX3 genes (Hauswirth et al., 2012).

4. Leopard Complex: Horses with this pattern, such as Appaloosa, may have distinctive facial markings with their coat pattern. The gene that causes the leopard spotting is TRPM1 and this can include facial spots (Neves et al., 2017).

5. Frame Overo: This pattern has patchy white patches which do not run across the back and may have facial markings. This is associated with the EDNRB gene which can result in significant white facial markings (Patterson Rosa et al., 2022).

Blue Eyes

The analysis of the coat patterns and facial markings shows that genetics is a crucial determinant of equine coat colour and patterns, and certain genes can lead to specific phenotypic characteristics such as blue eyes. Blue eyes are usually associated with certain white markings in horses. In particular, horses with the splashed white phenotype, which is characterized by significant white markings, have blue eyes. This is explained in the literature on the genetics of white spotting patterns where mutations in genes such as MITF and PAX3 are responsible for both the splashed white markings and blue eyes (Hauswirth et al., 2012; McFadden et al., 2024). Also, in the context of the Lethal White Overo (LWO) syndrome, which is also characterized by white markings, affected horses may have blue eyes as well (Neves et al., 2017).

Cut-outs, reverse colour, negative shapes, superimposed colour…

White marking with ‘cut-outs’, or the base color seen within the blaze, similar to freckles, but on a larger scale are inherited. The body color “cut-outs” in a white blaze on horses, which are the areas of depigmentation, are caused by mutations in genes regulating the migration and differentiation of melanocytes, the pigment producing cells of neural crest origin. Indeed, mutations in genes such as MITF (Microphthalmia-associated transcription factor) and PAX3 (Paired box gene 3) have been linked to splashed white phenotype, which is characterized by extensive white markings including “cut-outs” or unpigmented areas in the coat.

These traits are inherited in a composite fashion. Some white spotting patterns, including splashed white, can be inherited in an autosomal dominant manner; however, the expression can be highly variable among horses with similar pedigree because of the involvement of other genetic factors and possibly environmental factors. The inheritance is not strictly monogenic since many mutations can contribute to the final phenotype and thus result in varying levels of depigmentation from minimal to near complete (Hauswirth et al., 2012, Neves et al., 2017). In summary, the ‘cut-outs’ in a white blaze are the result of particular genetic mutations that affect the development of melanocyte and these traits can be inherited, but the inheritance is not very clear.

Variations in a horse’s health including skin and coat conditions may also affect how much white markings are seen in its life. In summary, the variability of white markings that come and go during the lifetime of a horse can be explained by the genetic, stochastic developmental events, environmental factors, and the health of the horse. Blazes or partial blazes on horses’ faces are forms of white facial marking and their inheritance is quite complex. According to Woolf (1990), the existence and intensity of white markings including blazes can be influenced by several genetic loci and the frequency of these markings may vary among the offspring depending on the genetic contribution of the parents (Woolf, 1990, p. 250-256). However, the patterns of white markings can be complicated and horses with highly marked parents may have more genes that cause white markings and tend to pass on similar markings to their offspring (Woolf, 1990, p. 250-256). Therefore, if a horse has a broken or incomplete blaze, it is possible that this trait can be inherited by their offspring, but the expression of the trait may vary depending on the genes of other factors. In conclusion, although there is a genetic basis for a broken or incomplete blaze, the specific expression in offspring is not fixed and may be influenced by other genes as well as environmental factors.

Clean edge or jagged?

The variation in the edges of facial markings in horses, whether they appear clean or jagged, can be attributed to several factors related to the developmental processes of melanoblasts, which are the precursor cells that develop into melanocytes (the cells responsible for pigment production).

1. Melanoblast Migration and Proliferation: The presence of clean or jagged edges in white markings is influenced by how well melanoblasts migrate and proliferate in the developing tissues. If melanoblasts migrate uniformly and proliferate evenly, the resulting markings tend to have clean edges. Conversely, if there are irregularities or disturbances in the migration and proliferation processes, this can lead to markings with jagged edges (Woolf, 1995).
2. Developmental Noise: The concept of “developmental noise” refers to random fluctuations that occur during the developmental processes. This can lead to variations in how melanoblasts settle in the skin and hair follicles. Such random events can result in differently shaped edges of the markings, with some being more defined (clean edges) and others being less so (jagged edges) (Woolf, 1995; Stachurska and Ussing, 2012).
3. Genetic Control: The genetic makeup of the horse also plays a significant role in how these markings develop. The interaction between multiple genes (a polygenic inheritance) affects the extent and pattern of white markings, which can contribute to differences in edge definitions (Woolf, 1990; Rieder et al., 2008).

Half a Head… Harlequin?

The phenomenon where a blaze covers half of a horse’s face in an almost perfect midline demarkation is probably due to the failure of melanoblasts (the cells that become melanocytes, which produce pigment) to migrate or proliferate properly during embryonic development. This migration and proliferation occurs independently on each side of the embryo to produce symmetrical or asymmetrical pigmentation patterns. If the melanoblasts on one side of the midline survive and proliferate while those on the other side do not, this can result in a clearly demarcated blaze covering one half of the face. This condition represents the concept of ‘developmental noise’, where random events during development can result in alterations in pigmentation. This explanation is in conformity with the fact that such markings are genetically and environmentally determined (Woolf, 1990; Mintz, 1967, 1974).

The Environment

The environment can greatly affect the phenotypic expression of white markings in horses through genetic and epigenetic changes. In particular, the migration and proliferation of melanoblasts, The cells that produce melanin in the skin are referred to as melanocytes and these cells migrate from the neural crest to their destination in the skin and hair follicles during early embryonic development. Specifically, the migration and proliferation of melanoblasts, The cells that produce melanin in the skin are referred to as melanocytes and these cells migrate from the neural crest to their destination in the skin and hair follicles during early embryonic development. However, environmental factors and epigenetic mechanisms can also affect the development and function of the melanocytes and, therefore, the formation of white markings. Stochastic factors that alter gene expression during early development can also influence the phenotype of the markings. Furthermore, epigenetic mechanisms such as DNA methylation, histone modifications, and microRNA activity can influence gene expression without affecting the underlying DNA sequence. The environment can also influence the development of white markings through stochastic (random) events that occur during melanoblast migration and survival. Epigenetic mechanisms such as DNA methylation and histone modifications can also affect gene expression in response to environmental factors. Together, these genetic and epigenetic mechanisms help to determine the pattern of white markings in horses. The environment can also cause white markings through stochastic (random) events that affect the development of melanoblasts and their survival.

Environmental factors can also influence gene expression through epigenetic mechanisms such as DNA methylation and histone modifications. These two types of mechanisms acting in concert determine the configuration of white markings in horses. The environment can also lead to white markings through stochastic (random) events that affect the development of melanoblasts and their survival. Epigenetic mechanisms can also be involved in the modulation of gene expression in response to environmental cues through DNA methylation and histone modifications. Both of these types of mechanisms are involved in the control of white markings in horses and they act in concert to determine the final outcome.

The most common markings

The most common marking in horses is the “half-stocking,” which was found to be present on the left back leg in a study of Arabian and Thoroughbred horses,with a prevalence of 16.9% (Kocakaya et al., 2023). The most common facial marking in horses is typically referred to as a “star.” In studies of various horse breeds, including Arabian horses, the presence of a star marking is frequently noted. For example, in the study by Woolf (1990), different types of facial markings were categorized, and the star marking is recognized as one of the common types observed.The prevalence of white facial markings was also mentioned in the Pura Raza Española horses, and it was mentioned that small white markings like stars are common (Encina et al., 2024).In general, depending on the breed, the star marking is one of the most common types of facial markings observed in horses.

Mustangs are not Native (no matter what you read online)

The primary genetic reason that mustangs in America are considered descendants of domestic horses rather than a native species is that their ancestry can be traced back to domesticated horses introduced to the Americas by Europeans, not to the prehistoric native horse species native to North America.

Key Genetic and Evolutionary Factors:

1.         Extinction of Native North American Horses     
Horses were first found in North America about 55 million years ago and evolved into many species. However, at the end of the Pleistocene epoch (~10,000 years ago) these horses went extinct, possibly due to climate change and hunting by early humans.This means that there was a genetic discontinuity – there were no wild horses in North America for a thousand of years.

2.         European Reintroduction of Domesticated Horses
 The modern Equus caballus was reintroduced to the Americas by the Spanish in the 16th century.  These horses were domesticated breeds from Europe and not the wild horses of North America.      Some escaped or were let loose, or were released, and formed feral populations, but their genetic origin is from the domestic stock, and not from the extinct native species.

3.         Genetic Studies Confirm Domestic Origins.  
DNA analysis of mustangs supports their origin from Iberian (Spanish) horse breeds, with later input from other European breeds (e.g. draft horses, Thoroughbreds). Their genetic continuity with the extinct North American horse species is absent.

4.         Legal and Conservation Classification
Mustangs are classified as feral and not wild because they are not genetically close to the prehistoric species and are descended from domesticated horses. The U.S. government has classified them as “wild” under the Wild Free-Roaming Horses and Burros Act of 1971, but from a biological point of view, they are domesticated feral animals and not a native species. In conclusion, mustangs in America are not considered to be a native species because they are descended from domesticated European horses and not the extinct native horses of prehistoric North America.

The following is a list of genetic traits that are present in true wild horses and are lacking in domestic horses: traits associated with domestication syndrome and the loss of genetic variability in regions linked to tameness and morphology. The analysis of the available data.

Key Genetic Differences Between Wild and Domestic Horses

1.         Greater Genetic Diversity in Przewalski’s Horse: The Przewalski’s horse (Equus ferus przewalskii) is considered the only living wild horse, that was never tamed. Genetic analyses show that Przewalski’s horses are genetically closer to their ancestral stock than domestic horses, especially with regard to the regions linked to the immune system and open environments. Librado et al. (2015) reported that Przewalski’s horses have unique haplotypes that are not seen in modern domestic horses, particularly at the mitochondrial DNA level.

2.         Distinct Y-Chromosome Haplotypes:  He noted that domesticated horses are suffering from a relative lack of variety at the Y-chromosome level as compared to true wild horses. Przewalski’s horses have a distinct Y-chromosome lineage that is not seen in domesticated horses (Lippold et al., 2011).

3.         Mutation in the G-Protein-Coupled Receptor (GPR143) Gene: In a study by Gaunitz et al. (2018), it was determined that domesticated horses have been selected at genes associated with behavior, including GPR143, which is linked to visual perception and may have influenced tameness. The true wild horses, including Przewalski’s, do not have these same mutations, suggesting a difference in their sensory perception and behavioral response.

4.         Genetic Selection in Neural and Behavioral Genes: Domesticated horses have been selected for tamer and less aggressive conformation through genes like SORCS1 and NRXN1 that are involved in the development of the nervous system and plasticity of synapses (Schubert et al., 2014).Przewalski’s horses do not have all of these domestication-related genetic changes and are therefore more skittish and less submissive than domestic breeds (Schubert et al., 2014).

5.         Difference in Coat Color Genetics: The ancient wild horses (including Przewalski’s) were mostly of the dun coat color, which is controlled by the TBX3 gene and is camouflage color. Many domestic horses lost the dun dilution allele in the process of selecting for other coat colors, whereas Przewalski’s horses retained this ancestral trait (Ludwig et al., 2009).

Horses, including the Przewalski’s horse, do not have the genetic markers of domestication that are seen in modern horses, especially in the behavioural, neurologic, and coat colour genes. They own higher Y-chromosome diversity, have conserved immune system genes, and have not been selectively bred for tameness and coat color, which makes them different from the domesticated breeds.

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Dr Meredith Hudes-Lowder & Karen McLain

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