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Article

Detection of Mycotoxins in Fallow Deer Milk and Feces: Evidence of Climate-Driven Contamination in a Comparative Study of Two Weather-Divergent Years in Hungary

by
István Lakatos
1,†,
Patrik Plank
2,†,
Arnold Tóth
2,
Zsófia Molnár
2,
Gabriella Skoda
2,
Szilamér Ferenczi
3,4,
Farkas Sükösd
5,6,
György Nagyéri
2,7,*,
László Szemethy
8 and
Zsuzsanna Szőke
2,*
1
Department of Regional Game Management, Ministry of Agriculture, 1052 Budapest, Hungary
2
Agribiotechnology and Precision Breeding for Food Security National Laboratory, Department of Animal Biotechnology, Institute of Genetics and Biotechnology, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary
3
Laboratory of Molecular Neuroendocrinology, Institute of Experimental Medicine, Hungarian Research Network, 1083 Budapest, Hungary
4
Department of Microbiology and Applied Biotechnology, Institute of Genetics and Biotechnology, Hungarian University of Agriculture and Life Sciences, 2100 Godollo, Hungary
5
Institute of Biochemistry, Hungarian Research Network, 6726 Szeged, Hungary
6
Department of Pathology, Péterfy Sándor Street Hospital, 1076 Budapest, Hungary
7
Department of Neurobiology and Szentágothai Research Centre, Institute of Biology, Faculty of Sciences, University of Pécs, 7624 Pecs, Hungary
8
Department of Agricultural Biology, Institute of Biology, Faculty of Sciences, University of Pécs, 7624 Pecs, Hungary
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Toxins 2026, 18(2), 93; https://doi.org/10.3390/toxins18020093
Submission received: 20 December 2025 / Revised: 23 January 2026 / Accepted: 5 February 2026 / Published: 11 February 2026
(This article belongs to the Section Mycotoxins)
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Abstract

Extreme weather impacts the ecological niches of fungi, altering mycotoxin risks in wildlife. We analyzed mycotoxin carry-over into European fallow deer (Dama dama) milk across seasons and assessed how drought influences the shift from Fusarium to Aspergillus mycotoxins and affects physiological resilience. Samples were collected during 2021/2022 and a drought-stricken 2022/2023 from South Transdanubia and Northeastern Hungary. Aflatoxin B1/M1 (AFB1/AFM1), Fumonisin B1 (FB1), Deoxynivalenol (DON), Zearalenone (ZEN), and Body Condition Scores (BCS) were measured to evaluate the impact of exposure on health status. The severe drought significantly altered the mycotoxin profile: ZEN levels declined significantly (from a median of 0.28 to 0.00 ng/mL), consistent with the moisture requirements of Fusarium graminearum, whereas DON concentrations increased. Concurrently, AFM1 persisted, exhibiting increased variance and extreme outliers in the maize-dominated South Transdanubian region. Distinct pharmacokinetic patterns were observed, and positive correlations were observed between milk and feces for lipophilic toxins, validating milk as a possible biomarker. Hydrophilic DON showed no correlation despite its accumulation. Emergence of “Poor” BCS group carrying loads supports “condition-dependent foraging” hypothesis, as stressed individuals are forced to consume contaminated resources, exacerbating oxidative stress and metabolic deficits.
Keywords:
fallow deer; milk; climate change; drought; Deoxynivalenol; Aflatoxin M1; Zearalenone; Fumonisin B1; body condition; bio-indicator; fecal mycotoxins
Key Contribution: The following can be highlighted. It is the first report of mycotoxin co-occurrence in milk of European wild fallow (Dama dama) deer. Severe drought shifted the mycotoxin profile by increasing DON levels while eliminating ZEN. Mycotoxin levels in milk showed a significant negative correlation with the body condition of lactating hinds. Milk analyses revealed that stressed individuals in poor conditions accumulated the highest mycotoxin loads.

Graphical Abstract

1. Introduction

Global climate change has already impacted all levels of the food (and food production) chains in recent decades, with one of the most complex and least predictable consequences being the transformation of the occurrence, quantitative distribution, and toxicological profile of fungal by-product mycotoxins [1,2,3]. Temperature anomalies, irregular precipitation patterns, droughts, and modifications to ecological niches favor the adaptation of some fungal species, allowing them to emerge in new geographical areas [4,5]. These environmental shifts indirectly but closely influence the toxicological potential and quality of milk, as well as the risk of human and animal (maternal and/or gastrointestinal) exposure to mycotoxins. Previous studies have indicated that drought conditions, often linked to climate change, significantly increase the prevalence of mycotoxigenic fungi (such as Aspergillus flavus and Fusarium species) and consequent mycotoxin contamination in the environment and animals [6,7]. Drought stress on crops elevates mycotoxin production, particularly aflatoxins, as fungi produce more toxins under environmental stress. This contamination adversely affects food safety, livestock health, and productivity, notably through feed and raw milk contaminated with mycotoxins [7,8]. However, key unknowns remain: comprehensive quantitative models predicting mycotoxin levels based on drought scenarios and regional climate conditions are lacking [6]. The synergistic effects of multiple environmental stressors, such as drought and heat, on fungal growth and toxin synthesis are not well understood. The long-term ecosystem-level impacts of recurrent droughts on fungal ecology and toxin accumulation require further study [6]. Additionally, the influence of fungal genetic variation and microbiome interactions on drought adaptation and consequent mycotoxin biosynthesis remains underexplored. Another gap exists in characterizing the physiological and metabolic responses of various animal species to increased mycotoxin exposure during drought periods [9]. Finally, developing and validating effective, sustainable mitigation strategies that are resilient to climate variability is still urgently needed [10].
This is particularly concerning because, in the absence of mitigation, the physiological burden of toxins directly impacts the organs responsible for secretion. Beyond simple carry-over, mycotoxins can directly affect the mammary glands by interfering with their normal immune function and physiology. Although detailed mechanistic studies on mammary gland impairment are limited, it is recognized that mycotoxin-induced immune suppression and metabolic disturbances in animals can translate into altered mammary gland physiology and compromised milk synthesis [11]. Mycotoxins negatively impact mammary gland health primarily through systemic physiological disruption and are actively transported into milk, leading to contamination of fawns [11]. Milk might be considered a particularly sensitive biological indicator of an active mycotoxin exposure. While the rumen microflora offers a degree of protection against certain mycotoxins, this barrier is not absolute; mycotoxins can be partially transformed during metabolic processes or excreted into milk in a changed or even unchanged form [12,13]. Consequently, milk can become not only a biological indicator of dietary exposure but also an integrated biomarker of the acting environmental and climatic stress.
Aflatoxins, produced by thermophilic Aspergillus species, are of primary concern to date. Under conditions of heat stress and drought, Aspergillus contamination in crops significantly increases [1,2]. In ruminants, the ingested aflatoxins, mainly Aflatoxin B1 (AFB1), are metabolically oxidized to Aflatoxin M1 (AFM1), which is excreted directly into milk [12,14]. Concurrently with the rise in aflatoxins, the dynamics of Fusarium toxins are also evolving. The most frequent fumonisin, the Fumonisin B1 (FB1), typically found in maize, may pass into milk in small amounts; however, climate-induced stress factors, such as oxidative stress and temperature fluctuations, can promote its biotransformation and excretion [15,16]. Similarly, Deoxynivalenol (DON), a trichothecene mycotoxin, is largely detoxified by rumen microbes under normal conditions. However, dysbiosis caused by nutritional changes or environmental stress can facilitate its systemic absorption and passage into milk [17,18]. DON is known to induce oxidative stress and impair the physiological condition of animals, further weakening their resilience. Furthermore, changing precipitation patterns favor the proliferation of Fusarium species producing Zearalenone (ZEN), an estrogenic mycotoxin. The toxin and its metabolites, particularly α-zearalenol, possess high biological activity and can accumulate in the fat phase of milk, posing risks of endocrine disruption [19,20]. Even T-2 and HT-2 toxins, previously associated with colder climates, are becoming more prevalent in temperate regions due to moderate but prolonged temperature rises [5,21]. To validate milk-based findings, the concurrent analysis of feces provides crucial insights into toxin exposure and the efficiency of ruminal detoxification [22,23]. Although ruminants are generally less sensitive to mycotoxins than monogastrics due to ruminal microbial degradation, this detoxification is often partial; for ZEN and AFB1, unmetabolized or partially metabolized toxins may persist in feces, particularly when intake exceeds the animal’s detoxification capacity [24,25]. Conversely, fumonisins exhibit low gastrointestinal absorption, making their high fecal excretion a reliable marker of recent exposure [22]. Given the co-occurrence of multiple toxins and potential environmental risks, fecal analysis is essential for effective risk management and animal health monitoring [26]. While the mechanisms of mycotoxin carry-over are well-documented in domestic animals, the dairy cattle [27], wild ungulates such as the European fallow deer (Dama dama) face these environmental pressures without the buffer of managed diets and/or technological mitigation. This might make them unique sentinels for monitoring “unfiltered” climate-driven contamination.
Our previous research has demonstrated the severity of exposure to mycotoxin, confirming the accumulation of mycotoxins in the liver of pregnant hinds and also their transplacental transfer to fetuses [28]. Consumed mycotoxins are generally processed, metabolized, partially accumulated, and/or removed and secreted by the feces. Building on this evidence of systemic toxicity, it is crucial to understand, inter alia, lactational transfer as an additional exposure route for offspring. The year 2022 brought a historic drought to Central Europe and especially Hungary, providing a natural experiment to assess these dynamics. This study offers a comparative analysis of mycotoxin appearance and co-occurrence (AFB1/AFM1, FB1, DON, ZEN) in fallow deer milk (with correlating the mycotoxin presence in feces) across two meteorologically distinct hunting seasons in Hungary.
Therefore, the objectives of this study were to (1) analyze the shift in mycotoxin profiles (AFB1/AFM1, FB1, DON, ZEN) in fallow deer milk across two weather-divergent seasons in Hungary, and to (2) correlate these levels with fecal excretion and host body condition. We hypothesized that severe drought would not only invert the fungal profile but also induce physiological stress that compromises the gut barrier, thereby validating wildlife milk as a sensitive, potential bioindicator of climate-driven environmental stress.

2. Results

A study was conducted on mycotoxin contaminations of fallow deer milk samples harvested at different Game Management Units (GMUs) during two consecutive hunting seasons (n = 43 in 2021/2022 and n = 38 in 2022/2023). Locations of GMUs are depicted in Figure 1 (and detailed in Section 5.2).
All the measurements were performed using specialized, commercial ELISA kits, following the instructions of the manufacturers and applying scientific standards for the evaluation of results.
Owing to the non-normal distribution of mycotoxin concentrations (Shapiro–Wilk test, p < 0.05), the data are presented as medians and interquartile ranges (IQR, Q1–Q3).

2.1. Seasonal Comparison of Mycotoxin Contaminations in Milk

A comparison of the two hunting seasons (2021/2022 and 2022/2023) revealed a distinct shift in the mycotoxin profile as described in Table 1 and Table 2 and depicted in Figure 2.
Briefly, we found the following. In the case of AFM1, no significant difference was observed between the two periods (Mann–Whitney U test, p = 0.49). In the 2021/2022 season, the median concentration was 44.78 pg/mL (IQR: 31.27–57.76), while in 2022/2023, it was 38.72 pg/mL (IQR: 18.90–65.14). However, the variance increased notably in the second year, with extreme values of 462.06 pg/mL.
In the case of FB1, a highly significant decrease was recorded in the second season (p < 0.001). The median concentration dropped from 18.48 ng/mL (IQR: 5.78–47.40) in 2021/2022 to 0.64 ng/mL (IQR: 0.00–10.50) in 2022/2023.
In the case of DON, in contrast to FB1, DON levels significantly increased (p = 0.014) during the drought year of 2022. The median concentration rose from 9.45 ng/mL (IQR: 4.69–12.10) to 12.86 ng/mL (IQR: 5.32–19.33).
ZEN showed the most drastic decline (p < 0.001). While in 2021/2022 the median was 0.28 ng/mL (IQR: 0.12–0.52), in the 2022/2023 season the median was below the limit of detection (0.00 ng/mL; IQR: 0.00–0.00), with only sporadic positive samples.

2.2. Regional Differences in Mycotoxin Contaminations in Milk

Regional analysis between South Transdanubia (GMU1-5) and northeast Hungary (GMU6) highlighted the spatial heterogeneity of mycotoxin exposures. Most of our findings are depicted in Figure 3.
Briefly, we found that in 2021/2022, the AFM1 levels were significantly higher (p < 0.001) in South Transdanubia GMU1-5 (Median: 49.11 pg/mL; IQR: 39.31–72.20) than in the Northeast region GMU6 (Median: 27.78 pg/mL; IQR: 20.54–32.46).
No significant regional differences were observed in the case of FB1, DON, or ZEN mycotoxins in milk.
In 2022/2023 season, the targeted mycotoxins did not show any statistically significant regional variances (p > 0.05).

2.3. Impact of Body Condition Scores

A comparison of BCS between the two hunting seasons revealed a significant deterioration in the health status of the population (chi2 = 11.10, p = 0.011). Data are summarized in Table 3.
In the 2021/2022 season, the population was predominantly in “Good” (58%) or “Excellent” (26%) condition, with no animals classified as “Poor.”
In contrast, during the drought-affected 2022/2023 season, the prevalence of animals in “Good” condition dropped to 36%, and a “Poor” condition group emerged, constituting 19% of the sampled population.
The analysis of the relationship between mycotoxin levels and BCS revealed distinct patterns in both years.
In the 2021/2022 season, a statistically significant association was found between DON levels and the BCS (Kruskal–Wallis test, p = 0.001), as mentioned in Table 4. The Bonferroni test could also show differences between BCS groups (Table 5).
Animals in the “Medium” condition exhibited significantly higher median DON concentrations (14.57 ng/mL) compared to those in “Good” (9.45 ng/mL) or “Excellent” (2.25 ng/mL) conditions. No significant correlation was observed for other toxins. In the 2022/2023 season, although the high variance prevented statistical significance (p > 0.05), a negative trend was observed for FB1 and DON. The “Poor” condition group presented the highest median FB1 (6.38 ng/mL) and DON (19.51 ng/mL) levels. Notably, animals in “Excellent” condition maintained a median FB1 level of 0.00 ng/mL, suggesting the potential avoidance of contaminated forage by dominant, healthy individuals.

2.4. Weather Data on Regions

Basic weather data (temperature, precipitation, rain–snow, etc.) in GMUs have been monitored daily. The related official data (provided by the National Weather Forecast Service in Hungary) are depicted in Figure 4 and Figure 5.
Briefly, 2021 was “dry and relatively cool”: drought occurred due to below-average precipitation, but it was not a record year for temperature. However, 2022 (even in the regions of GMUs) was “hot and extremely dry”, characterized by weather records, severe heat waves, and the lowest precipitation combined.

2.5. Correlation Between Milk and Fecal Mycotoxin Levels

To validate the dietary origin of the mycotoxins detected in milk, paired fecal samples collected during the 2021/2022 hunting season (n = 41) were analyzed.
In all examined cases, mycotoxin concentrations were significantly higher in feces than in milk, confirming that fecal excretion is the primary route of elimination. Notably, the amounts of mycotoxins measured in the feces (ZEN, DON, AFB1, and FB1) showed no significant differences across BCS groups (“Good”, “Medium”, “Excellent”), indicating consistent exposure through feed regardless of the physical conditions of animals.
Spearman correlation analysis revealed distinct patterns in the co-occurrence of mycotoxins in biological fluids and excreta, which were largely dependent on their physicochemical properties. Our main results are shown in Figure 6.
The strongest correlation was recorded for ZEN (r(40) = 0.44, p = 0.004), suggesting that for the apolar compounds, milk levels are closely linked to the quantity excreted in the feces. Similarly, moderate, statistically significant positive correlations were observed for FB1 (r(40) = 0.43, p = 0.005) and between the milk AFM1 and fecal AFB1 levels (r(41) = 0.35, p = 0.02). In contrast, no statistically significant correlation was found for the hydrophilic DON (r(41) = 0.15, p = 0.336), indicating different pharmacokinetic dynamics for water-soluble toxins compared to those for lipophilic ones.
The extreme weather conditions of the 2022/2023 season altered the fecal mycotoxin profile (Figure 7).
Consistent with the milk analysis, ZEN and FB1 were absent in the fecal samples, precluding the correlation analysis for these compounds as depicted in Figure 6.
For the detected mycotoxins, the relationship between milk and fecal levels showed divergent patterns in this study. A striking lack of correlation was observed for DON; despite its presence in milk, no statistical relationship was observed between milk and fecal concentrations (r(36) = 0.001, p = 0.981). Conversely, a moderate, statistically significant positive correlation persisted between milk AFM1 and fecal AFB1 levels (r(36) = 0.34, p = 0.039), suggesting that for aflatoxins, excretion pathways remained proportional to intake, even under environmental stress conditions.

2.6. Regional and Physiological Differences

Fecal analysis confirmed the regional heterogeneity observed in the milk, as illustrated with DON and AFB1 in Figure 8.
The South Transdanubian group (GMU1-5) exhibited significantly higher fecal AFB1 concentrations (median: 9.44 ng/g) than the Northeastern group GMU6 (median: 4.22 ng/g) (U = 78.00, p = 0.01). Regarding DON, although the median concentration was substantially higher in South Transdanubia (131.26 ng/g) than in the northeastern region (30.00 ng/g), the difference was not significant (U = 109.00, p = 0.105). Like the previous season, fecal shedding of mycotoxins did not vary significantly according to health status. The Kruskal–Wallis test showed no significant differences in fecal AFB1 (p = 0.803) or DON (p = 0.303) concentrations among the different BCS categories.

2.7. The DON Paradox: Evidence of Altered Absorption

A comparison of the two hunting seasons revealed a physiological anomaly in DON. While milk analysis indicated a significant increase in DON concentration during the drought year of 2022 (p = 0.014), fecal analysis did not reflect this increase.
There was no statistically significant difference in fecal DON concentrations between the 2021/2022 (Median: 153.00 ng/g) and 2022/2023 (Median: 115.22 ng/g) seasons (U = 645.00, p = 0.105). This discrepancy, higher levels in milk despite stable (or numerically lower) levels in feces, suggests a shift in pharmacokinetics.

3. Discussion

To our knowledge, this study represents the first comparative analysis of mycotoxin carry-over into fallow deer milk across two meteorologically distinct years. While mycotoxin exposure is well-documented in livestock, our findings highlight how extreme weather events—specifically, the historic drought of 2022—can fundamentally restructure the toxicological landscape for fallow deer. The data reveal a complex interaction between climatic shifts, fungal ecology, and host physiology, suggesting that drought does not merely reduce fungal activity but shifts the risk profile toward specific, persistent contaminants like DON and aflatoxins.

3.1. Ecological Drivers of the Altered Toxin Profile

A primary ecological finding was the inversion of the Fusarium toxin profile, with a significant increase in DON (p = 0.014) occurring alongside the near-total disappearance of ZEN (p < 0.001) during the drought year. Although a concurrent analysis of the exact forage consumed was not feasible in this free-ranging setting, this pattern is consistent with the known divergent ecological niches of Fusarium species [5]. Fusarium graminearum (DON producer) can exploit brief windows of rainfall during anthesis, even in warm climates, whereas ZEN production typically requires prolonged cool [4] and wet conditions that were absent in 2022. We hypothesize that the elevated DON load was further driven by a dietary shift toward drought-tolerant wild vegetation and forest edge forbs, which often serve as reservoirs for trichothecenes when pasture desiccation forces animals to broaden their foraging habits and consume these plants. Due to pasture desiccation, deer likely consumed wild grasses (Lolium, Festuca spp.) forbs surviving in forest edges, which are known reservoirs of trichothecenes, young shoots of woods, seeds, acorns, and wild fruits [18]. The importance of supplemental feeds is considerable, even though ‘natural nutrition’ can be the dominant source (constituting 60–80% of the total consumed nutrients, depending on age, sex, and habitat resources). The remaining 20–40% is provided as winter supplementation, primarily consisting of maize, which can ‘conserve’ mycotoxins from summer production. Indeed, an analysis [29] of the Alltech (Alltech, Inc., Nicholasville, KY, USA) report confirmed the presence of ZEN in the supplementary feed provided during the 2021 season. This suggests that while drought drives the ecological shift in wild flora, the ‘carry-over’ in stored feed remains a relevant exposure route during colder months.
Concurrently, the persistence of AFM1, particularly the extreme outliers observed in the maize-dominated South Transdanubian region, reinforces the projection that warming trends are expanding the ecological range of thermophilic Aspergillus species in Central Europe [14]. The persistence of AFM1 supports the hypothesis that warming trends are facilitating a northward shift in the ecological niches of Aspergillus fungi, increasing contamination pressure in Central and Eastern Europe [30,31]. Previous observations ranging from Italy to Hungary have highlighted the increasing expansion of Aspergillus flavus [32]. The 2012 drought in Serbia serves as a cautionary precedent, demonstrating how hot and arid weather conditions drive fungal proliferation in maize, ultimately leading to prolonged aflatoxin contamination that severely impacts the dairy sector [33]. The persistence of AFM1 in 2022, characterized by increased variance and extreme outliers (max: 462.06 pg/mL), supports the prediction that climate change facilitates Aspergillus colonization in Central Europe [2,34]. The significantly higher AFM1 levels in South Transdanubia (p < 0.001 in 2021) reflect the agricultural dominance of maize in this region. While drought inhibits the growth of Fusarium graminearum (a primary ZEN producer which requires moisture), the concurrent heat shock favors the proliferation of thermophilic Aspergillus species to produce aflatoxins (mainly AB1) in crops, which is then metabolically oxidized to (mainly) AFM1 and excreted in milk [12,14].
A specific risk factor identified in 2022 was the consumption of premature, drought-stressed acorns. The “forced drop” of acorns provided a contaminated food source that deer were compelled to utilize due to the lack of fresh grazing [35].

3.2. Physiological Implications: The “Leaky Gut” Hypothesis

The comparison of milk and fecal matrices revealed a critical pharmacokinetic discrepancy. For lipophilic toxins (ZEN, AFM1, FB1), we observed significant positive correlations between milk and feces (r = 0.34–0.44), confirming that milk concentrations generally reflect dietary intake and excretion dynamics [22]. However, DON presented a “diagnostic gap”: milk levels increased significantly during the drought, yet fecal excretion remained stable or numerically lower, resulting in a lack of correlation (r = 0.001) [17]. We suppose that this anomaly may be explained by compromised gut barrier function. Under optimal conditions, the rumen and intestinal epithelium limit the systemic absorption of hydrophilic toxins like DON [18]. However, environmental stress combined with suboptimal, high-fiber forage intake can induce dysbiosis and subclinical acidosis [36]. This scenario suggests a potential “leaky gut” mechanism where increased paracellular absorption directs DON into the bloodstream and milk, bypassing fecal elimination [37]. While histological confirmation of gut integrity was beyond the scope of this study, the data strongly support the utility of milk as a more sensitive bioindicator than feces for hydrophilic toxins in stressed populations [38].
Although DON is largely detoxified by rumen microflora under optimal conditions, environmental stressors such as heat stress and sudden dietary changes can impair this microbial barrier [13,17]. The increased systemic absorption observed in our study might suggest that the drought-induced stress compromised the detoxification capacity of the fallow deer microbiome. Our paired analysis of milk and fecal samples provides critical insights into the toxicokinetics of mycotoxins in wild ruminants. The significant positive correlations observed for ZEN (r = 0.44), FB1 (r = 0.43), and AFM1 (r = 0.35) confirm that for these compounds, milk concentrations are directly proportional to dietary intake and fecal excretion. This might validate the potential use of fallow deer milk (which is not merely a secretory product) as a biological matrix for monitoring environmental exposure.
The strength of these correlations appears linked to the physicochemical properties of the toxins; lipophilic compounds like ZEN [19] and AFM1 showed clearer input–output dynamics.
The significant positive correlation observed between fecal AFB1 and milk AFM1 concentrations (r = 0.34, p = 0.039) aligns with established metabolic models in ruminants. Early studies in lactating goats demonstrated that a consistent fraction of ingested, radiolabeled mycotoxin is metabolically converted and secreted into milk [39]. This is further supported by toxicokinetic research in lactating cows, which detailed the absorption, distribution, and excretion patterns of aflatoxins, confirming that absorbed AFB1 is rapidly distributed to the liver and subsequently excreted into milk as AFM1 [40].
Our field data indicate that this physiological link between dietary intake (reflected in feces) and lactational output remains robust in fallow deer, even under the environmental stress of a severe drought. However, the behavior of DON presents a notable exception to this pattern, creating a “diagnostic gap” between the two matrices.
The complete lack of correlation (r = 0.15 in 2021; r = 0.001 in 2022), combined with significantly elevated milk levels during the drought, suggests altered bioavailability. Under normal conditions, the rumen epithelium serves as an effective barrier against hydrophilic toxins. We can hypothesize that the severe drought stress and the likely consumption of high-fiber, subpar forage induced subclinical ruminal acidosis and dysbiosis, compromising the integrity of the gut barrier (tight junctions). This “leaky gut” phenomenon would facilitate the paracellular absorption of DON into the bloodstream and its subsequent transfer to milk, bypassing the fecal elimination route [17,36]. This mechanism explains why milk proved to be a more sensitive indicator of DON exposure than feces in the stressed population, highlighting also the necessity of multi-matrix monitoring in wildlife toxicology.

3.3. Body Condition and Conservation Implications

The significant decline in Body Condition Scores (BCS) during the drought season (p = 0.011) serves as a biological indicator of severe environmental stress. The emergence of a “Poor” condition group aligns with the cumulative effects of nutritional deficit and toxin exposure. The significant negative correlation between DON levels and body condition (p = 0.001) suggests that chronic exposure to trichothecenes has physiological consequences even at sub-clinical levels.
We propose that DON directly impairs energy storage by suppressing adipogenesis, the process of fat cell formation. Research indicates that DON significantly inhibits the expression of peroxisome proliferator-activated receptor gamma 2 (PPARSg2), a critical nuclear receptor isoform that functions as a master regulator of adipocyte differentiation and fat accumulation [41,42]. Inhibition of PPARSg2 disrupts the adipogenic program, preventing the maturation of fat cells and downregulating genes essential for lipid droplet maintenance, such as perilipin [43,44]. Consequently, this molecular disruption of lipid storage likely explains the reduced body fat mass and the significant deterioration in body condition observed in the DON-exposed population.
DON is known to induce oxidative stress and alter gut satiety hormones, leading to even feed refusal [18,45]. DON disrupts rumen fermentation and reduces the proportion of necessary “beneficial” bacteria (e.g., Lachnospiraceae) [36]. It has been demonstrated [46] that DON inhibits rumen fermentation in a dose-dependent manner, especially in high-fiber (high-forage) diets, which may also be typical of wild ruminants. Recent studies indicate that mycotoxin exposure can disrupt the gut microbiome and impair the barrier function (causing “leaky gut”), allowing the mycotoxins to enter systemic circulation [17,36,37]. This, inter alia, triggers a chronic inflammatory response that diverts metabolic energy away from adipose tissue storage, preventing animals from maintaining “Excellent” condition.
The trend observed in 2022, wherein animals in ‘Poor’ condition exhibited the highest FB1 and DON burdens, supports the ‘condition-dependent foraging’ hypothesis. This phenomenon illustrates a vicious cycle: during periods of resource scarcity, dominant individuals likely monopolize high-quality forage, thereby forcing subordinate or weakened individuals to graze on suboptimal vegetation or crop residues—known reservoirs of Fusarium mycotoxins. Although the transfer of FB1 to milk is typically low, stress factors such as oxidative stress can promote its biotransformation and excretion [9,10]. This creates a negative feedback loop in which poor nutritional status compels the intake of contaminated feed, further suppressing the animal’s immune system.
Beyond the immediate metabolic deficits observed in lactating hinds, the carry-over of mycotoxins into milk poses severe transgenerational risks for the offspring. Neonatal fawns are physiologically immature and rely entirely on maternal nutrients and immunoglobulins. The ingestion of milk containing immunomodulatory mycotoxins, such as DON and AFM1, likely compromises the developing immune competence. As highlighted by recent surveillance efforts in the region (Hungary), cervids can serve as critical sentinels for pathogens; however, their role as reservoirs is exacerbated when their immune defenses are weakened by environmental pollutant mycotoxins [47]. Consequently, fawns exposed to mycotoxins via lactation may exhibit higher susceptibility to emerging infectious diseases and increase the neonatal mortality rates.
Furthermore, the chronic immunosuppression and oxidative stress induced by mycotoxins—particularly DON—may act as a hidden predisposing factor for specific pathologies affecting skeletal and antler development. Weakened physiological status creates a gateway for opportunistic infections, such as Pedunculitis Chronica Deformans (PCD). This disease, which affects the pedicle periosteum and leads to severe antler malformations, has been linked to bacterial infections that thrive in immunocompromised hosts [48].
Our data suggests that climate-driven mycotoxin exposure could be an underlying “trigger factor” for PCD, particularly in yearling bucks (prickets/spikers). By compromising the general health of the population, drought-induced/modulated mycotoxin accumulation may indirectly contribute to the rising prevalence of early antler base diseases and trophy deformities in the sub-population.
Our study can have certain limitations that should be considered when interpreting the results. First, although the sample size is considered robust in the context of wildlife ecotoxicological studies, it is limited compared with controlled livestock experiments. Owing to the opportunistic nature of sampling (hunting), the data represent a cross-sectional “snapshot,” and thus, longitudinal monitoring of physiological changes within the same individuals was not possible. Second, as we investigated free-ranging ruminants, the exact feed intake and degree of toxin ingestion could not be directly measured. The dietary shift driven by drought, from agricultural crops to wild undergrowth, is a well-founded hypothesis, but it was not confirmed by a detailed botanical analysis of rumen content in this study. Third, the “leaky gut” mechanism was only hypothesized based on the pharmacokinetic discrepancy observed between milk and feces (specifically, the lack of correlation for DON). While this explanation is consistent with the literature, no histological analyses or specific intestinal permeability tests have been performed to directly verify the barrier function of the intestinal epithelium. Fourth, regarding the analytical methodology, we acknowledge that high-performance liquid chromatography (HPLC) or similar instrumental, chemical analyses can be optimal and/or the gold standard for mycotoxin quantification. However, we opted for commercial ELISA kits that were specifically certified and validated for milk matrices. We operated under the scientific assumption that the matrix properties of fallow deer milk are sufficiently similar to those of domestic ruminants to yield reliable results, particularly after the applied defatting step. ELISA offers distinct advantages in this context, providing a high-throughput, cost-effective, and reliable solution for screening large sample sizes in cases where access to specialized instrumental protocols for wild ruminant milk is limited. To ensure data integrity and mitigate the risk of false results, we applied rigorous quality control measures, including the use of available reference materials, internal spiking, and recovery calculations. The reliability of such rapid immunochemical methods for comparative biological monitoring of mycotoxins has been previously demonstrated in our related work [49].

4. Conclusions

This study highlights the potential of fallow deer milk as a bioindicator for monitoring environmental mycotoxin exposure. Our comparative analysis reveals that extreme drought fundamentally alters the mycotoxin profile, significantly increasing DON prevalence while eliminating ZEN. Crucially, the dissociation between elevated milk DON levels and stable fecal excretion suggests altered pharmacokinetics—likely due to stress-induced compromise of the gut barrier—making milk a more sensitive matrix for detecting hydrophilic toxins under such conditions. Furthermore, the significant association between high toxin loads and “Poor” body condition scores substantiates the “condition-dependent foraging” hypothesis, illustrating how environmental stress exacerbates exposure risks. Consequently, we recommend integrating milk analysis into wildlife health surveillance programs, particularly in regions prone to climatic extremes. Our findings align with the global framework proposed by The Lancet One Health Commission [38], demonstrating that milk analysis of wildlife can bridge the gap between climatology and veterinary epidemiology.

5. Materials and Methods

5.1. Ethics Approval and Consent to Participate

According to the statement of the Institutional Review Board (NAIK MBK MÁB 004–09/2018), the study is not considered an experiment with animals because the researchers collected samples from legally harvested (hunted) fallow deer.
Therefore, the ethical treatment rules are not applicable.

5.2. Areas of Investigation and Deer Targeted

Our research was conducted with the help/involvement of Game Management Units (GMUs) situated in different forested regions of Hungary.
Those areas were the following: GMU1: “Gyönk”; GMU2: “Törökkopány”; GMU3: “Kocsola”; GMU4: “Tamási”; GMU5: “Mészkemence” (at the village “Erdősmecske”), located in the South Transdanubian Hills; and GMU6: Gúth (at the village “Nyíradony”), located in the northeastern part of the country. Locations are depicted in Figure 1 (a proportional map of Hungary, “https://www.mapcustomizer.com/map/GMUs-Hungary (accessed on 15 December 2025)”).
According to the respective plans of these GMUs, the ecological environment and primary management characteristics were consistent/similar throughout the sampling areas.
GMU regions can be generally characterized by a continental climate, with an average annual temperature ranging from 9.5 to 11.5 °C. The average amount of annual rain and snow is between 500 and 650 mm, but with an uneven distribution. Droughts frequently occur, particularly during the summer period. Basic weather data (obtained from the Hungarian National Weather Forecast Service, www.metnet.hu) can be found in Figure 4 and Figure 5.
These habitats predominantly consist of broad-leaved, deciduous trees, and forests are dominated by oaks (Quercus robur, Q. cerris) and black locust (Robinia pseudoacacia), but are often surrounded by extensive agricultural lands where maize, wheat, sunflowers, and alfalfa are frequently cultivated crops.
The fallow deer is one of the predominant game species in GMUs in Hungary. Management practices focus on trophy hunting and the production and sale of venison. GMU practices include strict wildlife population control, habitat and game field management, and supplementary feeding from autumn to spring, consisting mainly of crops and silage predominated by maize. In this study, 4–8-year-old (“middle-aged”) deer have been targeted and used. The estimation of age is based on the experience of well-trained hunters of GMUs. Animals of this age have matured body size and structure, and high/highest reproductive performance (at peak).

5.3. Sampling

Sample collection was performed during the regular hunting seasons of December–January of 2021/2022 and 2022/2023.

5.3.1. Milk Harvesting

Milk samples were harvested from the hunted animals immediately after shooting during the evisceration process. Hunted does were visually inspected by hunters, experts, and only the individuals with intact udders were selected for sampling. Prior to collection, the teats/udder were thoroughly cleaned with antibacterial wet wipes. The first two streams of breast milk from each teat were always discarded to ensure flushing of the teat canals. The procedure is illustrated in Figure 9.
Subsequently, milk was collected into sterile, polypropylene 50 mL centrifuge tubes. On average, 10–15 mL of high-quality milk could be obtained from each animal per milking (harvesting). In our laboratory, the milk samples were aliquoted, frozen, and stored at −70 °C until the mycotoxin measurements.

5.3.2. Feces Collection

Feces from the colon were also sampled for mycotoxin analyses, after evisceration. The fecal sample was immediately placed in a cooler bag. After transportation to the laboratory, the samples were aliquoted and stored at −70 °C until analyses were performed.

5.4. Mycotoxin Measurements from Fallow Deer Milk

5.4.1. AFM1 Analyses

A commercial enzyme-linked immunosorbent assay (ELISA) product based on a quantitative competitive immunoassay, the RIDASCREEN Aflatoxin M1 ELISA Kit (cat. No. R1121, R-Biopharm AG, Darmstadt, Germany) was employed to detect AFM1 in fallow deer milk samples.
Because of assay features described below, and the fact that the predominant aflatoxin in milk is AFM1, measured values were considered as AFM1 content of samples.
The kit is validated for the measurement of AFM1 in milk samples of domestic animals such as cattle. The limit of detection (LOD) of the kit for AFM1 was 5 ng/L, with a selectivity of 100% for AFM1 and a cross-reactivity of less than 10% for AFM2. The procedure was generally applied as instructed by the manufacturer. The limit of quantification (LOQ) value was not provided by the manufacturer.
The test kit included six AFM1 standard solutions in a milk buffer (0, 5, 10, 20, 40, and 80 ng/L), an anti-AFM1 antibody (concentrate), a conjugate (peroxidase-conjugated AFM1, concentrate), a substrate/chromogen (tetramethylbenzidine, TMB with H2O2), a stop solution (1 N H2SO4), a dilution buffer, and buffer salt, which was dissolved in 1000 mL of distilled water for washing steps.
Optical density was measured using a Thermo MultiskanTM FC microplate reader (Waltham, MA, USA) equipped with SkanIt RE software (version 6.1.1.7). Absorbance was measured at 450 nm with a reference wavelength of 630 nm.
Samples exceeding the 80 ng/L AFM1 level were (again) diluted with kit dilution buffer, and the diluted sample was re-assayed.
All the samples were assayed in duplicates (generally, for all mycotoxins).
The AFM1 recovery rates were determined at three concentration levels using the ERMBD283 whole milk powder material with a reference value of 0.111 µg/kg AFM1 (with 0.0018 µg/kg uncertainty) and ERMBD282 under 0.02 µg/kg obtained from the Joint Research Centre, Institute for Reference Materials and Measurements (Geel, Belgium).
Internal and additional spiking with AFM1 reference solution into some samples (and calculation of recovery of spiked amounts) was also applied.

5.4.2. FB1 Analyses

A commercial ELISA product based on competitive immunoassay, EuroProxima Fumonisin ELISA Kit (cat. No. 5121FUM, R-Biopharm Nederland B.V., Arnhem, the Netherlands) was used to detect fumonisins (such as FB1) in fallow deer milk samples. The kit is certified for the measurement of fumonisins in milk samples of frequently domesticated animals. The predominant fumonisin mycotoxin was FB1; the measured values were considered as the FB1 content of the samples.
The LOD of the kit for milk was 1 ng/mL, with cross-reactivities of 100% for FB1, and 27% and 76% for fumonisin B2 and B3, respectively. The limit of quantification (LOQ) value was not provided by the manufacturer. The test kit included six standard FB1 solutions (0.125, 0.25, 0.5, 1.0, 2.0, and 4.0 ng/mL), a zero standard, antibody solution, a conjugate (peroxidase-conjugated FB1), a substrate/chromogen solution (H2O2/TMB), a stop solution, a dedicated sample dilution buffer, and a rinsing buffer. The test was conducted according to the manufacturer’s instructions.
Milk samples were prepared by centrifuging thawed, cold samples for 15 min at 2000× g and 4 °C, and the upper fat layer was separated and removed. A 0.5 mL aliquot of defatted milk was transferred to another test tube and mixed with 4.5 mL of 20% (v/v) methanol in the dilution buffer. Optical density was measured at 450 nm (with a reference wavelength at 630 nm) using a microplate reader. The fumonisin equivalents obtained from the calibration curve were multiplied by a factor of dilution for the milk samples.
LOD of assay was 0.05 µg/L. Internally and additionally spiking with FB1 reference solution into some samples (and recovery of spiked amounts) was also applied.

5.4.3. ZEN Analyses

ZEN in milk was assessed using a commercially available, competitive ELISA immunoassay-based product RIDASCREEN Zearalenone Quantitative Test Kit (Art. No.: R1401, R-Biopharm AG, Darmstadt, Germany). The kit is validated for the measurement of ZEN in milk samples of frequently domesticated animals. The kit showed a cross-reactivity of 41.6% with α-Zearalenol, 27.7% with Zeranol, and 13.8% with β-Zearalenol. LOD of assay was 0.05 µg/L. The limit of quantification (LOQ) value was not provided by the manufacturer.
An extraction was performed according to the manufacturer’s instructions. Thawed milk samples were centrifuged at 3000 rpm at 4 °C for 15 min. The upper cream layer was subsequently removed. To 1 mL of the sample, 20 μL of a glucuronidase/arylsulfatase enzyme from Helix pomatia (Merck, BGALA-RO, Budapest, Hungary) was added and incubated for 3 h at 37 °C. As a post-incubation, 0.1 mL of methanol was added to 0.9 mL of the hydrolyzed, defatted, and digested milk, and 50 μL of the resultant solution was used in the assay. Standards (final concentrations) were freshly prepared each day in skimmed milk containing 10% (v/v) methanol. The assay procedure was also performed according to the manufacturer’s instructions. Absorbance was measured at 450 nm (with a reference wavelength at 630 nm) using a microplate reader. Spiking as an additional control for our measurements was also applied.

5.4.4. DON Analyses

For the measurement of DON, a commercial, competitive immunoassay-based kit, the Bio-Shield DON 5 ELISA Kit (cat. No. B5248/B5296, ProGnosis Biotech S.A., Larissa, Greece), was used as instructed.
The kit is nominated for the measurement of DON in milk samples of frequently domesticated animals. The antibody of the kit exhibited cross-reactivity of >100% with 15-acetyl-DON and 100% with 3-acetyl-DON, whereas cross-reactivity with Nivalenol and Patulin was negligible (<1%). LOD of assay was 0.07 µg/L. The limit of quantification (LOQ) value was not provided by the manufacturer. The kit provides a quantitative analysis of DON in a 0 to 5 ppm range. Thawed milk samples were prepared by centrifuging for 10 min at 3000× g to remove the upper fat layer. A 1 mL aliquot of defatted milk was subsequently diluted with 4 mL of 17.5% (v/v) methanol and mixed using a vortex mixer. A 100 µL volume of this diluted solution was used directly for the immunoassay (in a well of a plate). Optical density was measured at 450 nm (with a reference wavelength at 630 nm) using a microplate reader. To calculate the final concentration in the milk samples, the DON results obtained from the calibration curve were also factored by the dilutions applied. Spiking of the same milk samples as internal controls for measurement was applied.

5.5. Mycotoxin Measurements from Feces

All the procedures were performed as applied and published earlier. Instrumental High-performance liquid chromatography (HPLC) and liquid chromatography–mass spectrometry (LC-MS) analyses were conducted.
Fecal levels of FB1, DON, ZEN, and AFB1 were measured using a Shimadzu LCMS-2020 single-quadrupole mass spectrometer equipped with an ESI ion source (Shimadzu, Kyoto, Japan). The extraction, chromatographic separation, and MS parameters for FB1, DON, and ZEN were executed precisely following the protocol validated for fallow deer as outlined in our previous study [37]. All reagents and chemicals used in these analyses were of LC-MS grade. For AFB1, the samples were prepared using a modified Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) extraction method [50], as detailed in the same research [37].
To measure DON, ZEN, and FB1, samples were brought to room temperature, and 1-1 g of each was combined with 4-4 mL of a solution consisting of acetonitrile, water, and acetic acid in a 50:50:0.1 volume ratio within a tube. The tubes underwent extraction in an ultrasonic water bath for 15 min, followed by shaking at 400 rpm for 120 min, and then centrifugation at 4000 rpm for 5 min. A 1.5 mL portion of the supernatant was collected and further centrifuged in a separate tube at 14,000 rpm for 10 min at 4 °C. The supernatant was then filtered using a syringe filter with a 0.45 μm pore size and analyzed with an LC-MS device. The samples were separated using a prominence-type HPLC system (Shimadzu, Kyoto, Japan) equipped with a Kinetex XB-C18 column (100 mm length, 2.1 mm internal diameter, 2.6 μm particle diameter). For gradient elution, 1% acetic acid in water (eluent A) and 1% acetonitrile (eluent B) were used at a flow rate of 0.3 mL/min with the column maintained at 40 °C. The gradient profile was as follows: the initial composition of 5% eluent B was held for 1 min. From 1 to 3 min, the proportion of eluent B was linearly increased from 5% to 60%, and then held at 60% from 3 to 4 min. Between 4 and 8 min, it was linearly increased from 60% to 95%, and maintained at 95% from 8 to 10.9 min. The initial conditions were restored by reducing the eluent B proportion from 95% to 5% between 10.9 and 12.5 min, and then maintaining it at 5% for 2.5 min, resulting in a total separation time of 15 min per sample, including final recalibration. The sample injection volume was consistently 10 μL. The mass spectrometer used was an LCMS-2020 (Shimadzu, Kyoto, Japan) single-quadrupole mass spectrometer with an ESI ion source. The m/z values for quantifying each toxin were determined by injecting 100 mg/L standard solutions in scan mode: FB1 had a retention time of 4.62 min with an m/z value of 722.4 (positive ion mode), DON had a retention time of 2.70 min with an m/z value of 355.0 (negative ion mode), and ZEN had a retention time of 5.78 min with an m/z value of 317.0 (negative ion mode). To quantify the toxins, an external standard calibration method was employed, with a linearity range of 0.004–4 mg/kg per unit sample mass. Samples exceeding this concentration were re-analyzed following suitable dilution. The limits of detection (LOD) for FB1, DON, and ZEN were 0.013, 0.033, and 0.003 mg/kg, respectively, while the limits of quantification (LOQ) were 0.040, 0.098, and 0.004 mg/kg, respectively.
To extract AFB1, the procedure was as follows: 4 mL of a 2% formic acid solution in a 1:1 volume ratio of MeCN and H2O was added to 1 g of the sample. This mixture was then agitated using a horizontal vortex at 320 rpm for 15 min. Next, 0.8 g of anhydrous MgSO4 and 0.2 g of NaCl were introduced, followed by immediate vortexing for 60 s. The resulting mixture was centrifuged at 4000 rpm for 5 min. Afterward, 500 μL of the upper layer, which contained MeCN, was diluted to 1 mL with purified water and then filtered using a 0.22 μm membrane filter. An aliquot of the filtered solution was subsequently used for HPLC-MS analysis. The samples were analyzed using a single quadrupole LC-MS system (LCMS-2020, Shimadzu, Kyoto, Japan). Separation was achieved with a Kinetex XB-C18 reversed-phase column (100 mm × 2.1 mm, 2.6 μm; Phenomenex (Torrance, CA, USA)) at a flow rate of 0.2 mL/min. The injection volume was set at 10 μL, and the column temperature was maintained at 30 °C. Mobile phase A consisted of 0.1% (v/v) formic acid and 5 mmolL-1 ammonium formate in LC-MS-grade water, while mobile phase B was LC-MS-grade methanol. The gradient elution program began with 10% B, increasing linearly to 60% over 3 min, then from 60% to 100% over the next 5 min. The column was then washed with 100% B for 3 min, after which the gradient was reduced back to the initial 10% B within 1 min, followed by a 3 min re-equilibration. Electrospray ionization was conducted in positive mode (ESI+), with a capillary voltage of 4.5 kV. Nitrogen was used as the cone, nebulizing, and desolvation gas. The ion source temperature was set at 250 °C. AFB1 was measured at three different m/z values, with one used for quantification and two for identity verification. The retention time for AFB1 was 7.30 min, with the target ion at m/z 313.0 and two confirmatory ions at m/z 350.9 and 663.171. The LOD and LOQ for AFB1 were determined to be 0.0033 mg/kg and 0.100 mg/kg, respectively [37].

5.6. Determination of Body Condition Scores of Animals

Body Condition Scoring (BCS) for hunted animals is a widely used visual/tactile system (usually assessing fat/muscle to gauge health); hunters, experts and the examined GMUs use it post-mortem on fallow deer and all species on Cervidae, by feeling ribs/spine/pelvis to determine if an animal is too thin (emaciated) or has ample fat reserves, impacting meat quality, management decisions, and understanding population health, especially with seasonal or habitat changes. The same scoring system of GMUs was used on all animals.
The following groups were created based on parameters examined: “Excellent”, “Good”, “Medium”, and “Poor”.

5.7. Statistical Analysis

Statistical evaluation was performed using “numiqo: Online Statistic Calculator” software system (numiqo e.U. Graz, Austria. URL https://numiqo.com).
The normality of the data distribution was assessed using the Shapiro–Wilk test. Due to the non-normal distribution of mycotoxin concentrations (p < 0.05), descriptive statistics are reported as medians and interquartile ranges (IQR, Q1–Q3), along with minimum and maximum values in the text.
To compare mycotoxin levels between the two hunting seasons (2021/2022 vs. 2022/2023) and between the two geographical regions of GMUs (South Transdanubia vs. Northeastern Hungary), the non-parametric Mann–Whitney U test was employed.
The relationship between mycotoxin concentrations and BCS categories (Excellent, Good, Medium, Poor) was analyzed using the Kruskal–Wallis H test. Where significant differences were detected, pairwise comparisons were performed using Dunn’s post hoc test with Bonferroni correction. Changes in the prevalence of BCS categories between the two study periods were evaluated using the chi-square (chi2) test for independence.
For all statistical analyses, a probability value of p < 0.05 was considered statistically significant.

Author Contributions

Conceptualization, Z.S. and L.S.; methodology, P.P., A.T., Z.M., G.S., I.L. and S.F.; software, Z.S. and G.N.; validation, F.S., G.S., S.F., L.S. and Z.S.; formal analysis, Z.S., G.N. and L.S.; investigation, I.L., S.F. and Z.S.; resources, I.L. and Z.S.; data curation, Z.S., G.N. and L.S.; writing—original draft preparation, Z.S., P.P., L.S. and G.N.; writing—review and editing, G.N., Z.S., P.P. and L.S.; visualization, G.N. and Z.S.; supervision, Z.S.; project administration, Z.S.; funding acquisition, I.L. and Z.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Department of Game Management, Ministry of Agriculture, and granted by the National Research, Development, and Innovation Office of the Hungarian Government (grant no: RRF 2.3.1 21 2022–00007 for Agribiotechnology and Precision Breeding for Food Security National Laboratory) and the Flagship Research Groups Programme of the Hungarian University of Agriculture and Life Science (Flagship Research Groups 2026, EcoHealth Research Group).

Institutional Review Board Statement

Not applicable. According to the statement of the Institutional Review Board (NAIK MBK MÁB 004–09/2018), the study is not considered an experiment with animals because the researchers collected samples from legally harvested (hunted) fallow deer.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available upon request from the corresponding authors.

Acknowledgments

The authors wish to express their gratitude to Szabolcs Döbrösy, Péter Szabó-Tóth, Evelin Imre, Péter Gőbölös, Gábor Palánki, and Katalin Posta for their work enabling the successful implementation of our study.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AFB1Aflatoxin B1
AFM1Aflatoxin M1
BCSBody Condition Scoring
DONDeoxynivalenol
ELISAEnzyme-linked immunosorbent assay
FB1Fumonisin B1
GMUGame Management Unit
HPLCHigh-performance liquid chromatography
IQRInterquartile ranges
LODLimit of detection
LCLiquid chromatography
MSMass spectrometry
PCDPedunculitis Chronica Deformans
PPARSg2Peroxisome proliferator-activated receptor gamma 2
TMBTetramethylbenzidine
v/vVolume/volume percent

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Figure 1. Locations of Game Management Units in Hungary: 1. GMU1 (Gyönk); 2. GMU2 (Törökkoppány); 3. GMU3 (Kocsola); 4. GMU4 (Tamási); 5. GMU5 (Mészkemence); 6. GMU6 (Gúth). “https://www.mapcustomizer.com/map/GMUs-Hungary (accessed on 15 December 2025)”.
Figure 1. Locations of Game Management Units in Hungary: 1. GMU1 (Gyönk); 2. GMU2 (Törökkoppány); 3. GMU3 (Kocsola); 4. GMU4 (Tamási); 5. GMU5 (Mészkemence); 6. GMU6 (Gúth). “https://www.mapcustomizer.com/map/GMUs-Hungary (accessed on 15 December 2025)”.
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Figure 2. Comparison of mycotoxin concentrations in fallow deer milk samples collected during two consecutive hunting seasons (in season 2021/2022, n = 43; and in 2022/2023, n = 38). (A) AFM1, (B) FB1, (C) DON, (D) ZEN. Data are presented as box plots with individual data points. The box represents the interquartile range (IQR), the horizontal line within the box marks the median, and the whiskers extend to 1.5 times the IQR. FB1 is plotted on a logarithmic scale because of the wide range of concentrations. p-values indicate significant differences between seasons based on the Mann–Whitney U test ({*} p < 0.05, {***} p < 0.001, ns: not significant). Y-axes represent mycotoxin concentrations for FB1, DON, and ZEN in ng/mL, or in pg/mL for AFM1. Abbreviations: AFM1, Aflatoxin M1; FB1, Fumonisin B1; DON, Deoxynivalenol; ZEN, Zearalenone.
Figure 2. Comparison of mycotoxin concentrations in fallow deer milk samples collected during two consecutive hunting seasons (in season 2021/2022, n = 43; and in 2022/2023, n = 38). (A) AFM1, (B) FB1, (C) DON, (D) ZEN. Data are presented as box plots with individual data points. The box represents the interquartile range (IQR), the horizontal line within the box marks the median, and the whiskers extend to 1.5 times the IQR. FB1 is plotted on a logarithmic scale because of the wide range of concentrations. p-values indicate significant differences between seasons based on the Mann–Whitney U test ({*} p < 0.05, {***} p < 0.001, ns: not significant). Y-axes represent mycotoxin concentrations for FB1, DON, and ZEN in ng/mL, or in pg/mL for AFM1. Abbreviations: AFM1, Aflatoxin M1; FB1, Fumonisin B1; DON, Deoxynivalenol; ZEN, Zearalenone.
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Figure 3. Regional differences in mycotoxin concentrations in milk samples across the two hunting seasons. In South Transdanubia, GMU1-5, n = 31 in 2021, n = 25 in 2022; in northeast Hungary, GMU6, n = 12 in 2021, n = 13 in 2022. (A) AFM1, (B) FB1, (C) DON, (D) ZEN. Boxplots show the distribution of toxin levels in each region during each season. Statistical significance between regions within the same season was determined using the Mann–Whitney U test and is indicated by brackets ({***} p < 0.001, ns: not significant). FB1 concentrations are presented on a logarithmic scale. Y-axes represent mycotoxin concentrations for FB1, DON, and ZEN in ng/mL, or in pg/mL for AFM1. Abbreviations: AFM1, Aflatoxin M1; FB1, Fumonisin B1; DON, Deoxynivalenol; ZEN, Zearalenone.
Figure 3. Regional differences in mycotoxin concentrations in milk samples across the two hunting seasons. In South Transdanubia, GMU1-5, n = 31 in 2021, n = 25 in 2022; in northeast Hungary, GMU6, n = 12 in 2021, n = 13 in 2022. (A) AFM1, (B) FB1, (C) DON, (D) ZEN. Boxplots show the distribution of toxin levels in each region during each season. Statistical significance between regions within the same season was determined using the Mann–Whitney U test and is indicated by brackets ({***} p < 0.001, ns: not significant). FB1 concentrations are presented on a logarithmic scale. Y-axes represent mycotoxin concentrations for FB1, DON, and ZEN in ng/mL, or in pg/mL for AFM1. Abbreviations: AFM1, Aflatoxin M1; FB1, Fumonisin B1; DON, Deoxynivalenol; ZEN, Zearalenone.
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Figure 4. Heatmap-like illustration of some temperature data from 2021, 2022, and 2023. (Left) Annual mean temperature in 2021 (A), 2022 (B), and 2023 (C). Scales applied (D). (Right) Annual mean temperature deviation from the multi-year average. All data given in Celsius. “https://www.met.hu/eghajlat/magyarorszag_eghajlata/eghajlati_visszatekinto/elmult_evek_idojarasa/ (accessed on 15 December 2025)”.
Figure 4. Heatmap-like illustration of some temperature data from 2021, 2022, and 2023. (Left) Annual mean temperature in 2021 (A), 2022 (B), and 2023 (C). Scales applied (D). (Right) Annual mean temperature deviation from the multi-year average. All data given in Celsius. “https://www.met.hu/eghajlat/magyarorszag_eghajlata/eghajlati_visszatekinto/elmult_evek_idojarasa/ (accessed on 15 December 2025)”.
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Figure 5. Heatmap-like illustration of some precipitation data from 2021, 2022, and 2023. (Left) Annual mean precipitation in 2021 (A), 2022 (B), and 2023 (C). Scales applied (D). (Right) Annual mean precipitation deviation from the multi-year average. All data given in mm. “https://www.met.hu/eghajlat/magyarorszag_eghajlata/eghajlati_visszatekinto/elmult_evek_idojarasa/ (accessed on 15 December 2025)”.
Figure 5. Heatmap-like illustration of some precipitation data from 2021, 2022, and 2023. (Left) Annual mean precipitation in 2021 (A), 2022 (B), and 2023 (C). Scales applied (D). (Right) Annual mean precipitation deviation from the multi-year average. All data given in mm. “https://www.met.hu/eghajlat/magyarorszag_eghajlata/eghajlati_visszatekinto/elmult_evek_idojarasa/ (accessed on 15 December 2025)”.
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Figure 6. Correlations between milk and feces mycotoxin levels. Feces mycotoxin levels (given in ng/g) correlated with milk levels (given ng or pg/mL, respectively). (A) ZEN in samples from season 2021/2022 (r(40) = 0.44, p = 0.004), (B) FB1 in samples from season 2021/2022 (r(40) = 0.43, p = 0.005), (C) AFB1/AFM1 in samples from season 2021/2022 (r(41) = 0.35, p = 0.02), (D) AFB1/AFM1 in samples from season 2022/2023 (r(36) = 0.34, p = 0.038), (E) DON in samples from season 2021/2022 (r(41) = 0.15, p = 0.336), (F) DON in samples from season 2022/2023 (r(36) = 0.00, p = 0.981). Spearman’s rank correlation was used. Abbreviations: AFM1, Aflatoxin M1; FB1, Fumonisin B1; DON, Deoxynivalenol; ZEN, Zearalenone.
Figure 6. Correlations between milk and feces mycotoxin levels. Feces mycotoxin levels (given in ng/g) correlated with milk levels (given ng or pg/mL, respectively). (A) ZEN in samples from season 2021/2022 (r(40) = 0.44, p = 0.004), (B) FB1 in samples from season 2021/2022 (r(40) = 0.43, p = 0.005), (C) AFB1/AFM1 in samples from season 2021/2022 (r(41) = 0.35, p = 0.02), (D) AFB1/AFM1 in samples from season 2022/2023 (r(36) = 0.34, p = 0.038), (E) DON in samples from season 2021/2022 (r(41) = 0.15, p = 0.336), (F) DON in samples from season 2022/2023 (r(36) = 0.00, p = 0.981). Spearman’s rank correlation was used. Abbreviations: AFM1, Aflatoxin M1; FB1, Fumonisin B1; DON, Deoxynivalenol; ZEN, Zearalenone.
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Figure 7. DON in milk and faces in samples from 2021/2022 and 2022/2023. Milk and feces DON levels (given in ng/mL or ng/g). (A) DON in samples from season 2021/2022, (B) DON in samples from season 2022/2023. Abbreviations: DON, Deoxynivalenol.
Figure 7. DON in milk and faces in samples from 2021/2022 and 2022/2023. Milk and feces DON levels (given in ng/mL or ng/g). (A) DON in samples from season 2021/2022, (B) DON in samples from season 2022/2023. Abbreviations: DON, Deoxynivalenol.
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Figure 8. Regional differences in AFB1 and DON concentrations in feces samples between regions, across the two hunting seasons. In South Transdanubia, GMU1-5, n = 31 in 2021, n = 25 in 2022; in northeast Hungary, GMU6, n = 12 in 2021, n = 13 in 2022. Boxplots show the distribution of toxin levels in each region during each season. (A) AFB1 in feces in regions (2021/2022); (B) AFB1 in feces in regions (2022/2023); (C) DON in feces in regions (2021/2022); (D) DON in feces in regions (2022/2023); Statistical significance between regions within the same season was determined using the Mann–Whitney U test. FB1 concentrations are presented on a logarithmic scale. Abbreviations: AFB1, Aflatoxin B1; DON, Deoxynivalenol.
Figure 8. Regional differences in AFB1 and DON concentrations in feces samples between regions, across the two hunting seasons. In South Transdanubia, GMU1-5, n = 31 in 2021, n = 25 in 2022; in northeast Hungary, GMU6, n = 12 in 2021, n = 13 in 2022. Boxplots show the distribution of toxin levels in each region during each season. (A) AFB1 in feces in regions (2021/2022); (B) AFB1 in feces in regions (2022/2023); (C) DON in feces in regions (2021/2022); (D) DON in feces in regions (2022/2023); Statistical significance between regions within the same season was determined using the Mann–Whitney U test. FB1 concentrations are presented on a logarithmic scale. Abbreviations: AFB1, Aflatoxin B1; DON, Deoxynivalenol.
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Figure 9. Milk sampling from hunted fallow deer.
Figure 9. Milk sampling from hunted fallow deer.
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Table 1. Mycotoxin concentrations in samples harvested in the 2021/2022 season.
Table 1. Mycotoxin concentrations in samples harvested in the 2021/2022 season.
2021/2022
Mycotoxins in MilkRegionSamplesMedianStd. DeviationMin.Max.Mean ± Std.
AFM1 (pg/mL)South Transdanubian area (GMU1-5)3149.1148.3621.47238.1266.37 ± 48.36
Northeast (GMU6)1227.7813.18045.2725.66 ± 13.18
FB1 (ng/mL)South Transdanubian area (GMU1-5)3120.2542.990153.4138.12 ± 42.99
Northeast (GMU6)1214.5462.50207.4738.14 ± 62.5
DON (ng/mL)South Transdanubian area (GMU1-5)319.976.92030.179.8 ± 6.92
Northeast (GMU6)128.676.08015.947.38 ± 6.08
ZEN (ng/mL)South Transdanubian area (GMU1-5)310.340.2901.020.39 ± 0.29
Northeast (GMU6)120.150.2200.690.23 ± 0.22
Table 2. Mycotoxin concentrations in samples harvested in the 2022/2023 season.
Table 2. Mycotoxin concentrations in samples harvested in the 2022/2023 season.
2022/2023
Mycotoxins in MilkRegionSamplesMedianStd. DeviationMin.Max.Mean ± Std.
AFM1 (pg/mL)South Transdanubian area (GMU1-5)2547.7591.370462.0669.96 ± 91.37
Northeast (GMU6)1323.3736.760114.0735.02 ± 36.76
FB1 (ng/mL)South Transdanubian area (GMU1-5)252.3712.93055.418.11 ± 12.93
Northeast (GMU6)1303.01010.691.39 ± 3.03
DON (ng/mL)South Transdanubian area (GMU1-5)2513.8719.892.749817.51 ± 19.89
Northeast (GMU6)1311.8611.980.9333.5215.17 ± 11.98
ZEN (ng/mL)South Transdanubian area (GMU1-5)2500.0300.150.01 ± 0.03
Northeast (GMU6)1300.1100.380.04 ± 0.11
Table 3. Comparison of body condition score (BCS) distributions in fallow deer between the 2021/2022 and 2022/2023 hunting seasons.
Table 3. Comparison of body condition score (BCS) distributions in fallow deer between the 2021/2022 and 2022/2023 hunting seasons.
SeasonSamplesExcellentGoodMediumPoorTestStatisticp-Value
2021/20224326%58%16%0
2022/20233633.5%36.5%11%19%Chi-square11.10.011
Table 4. Further comparison of body condition score (BCS) distributions in fallow deer between the 2021/2022 and 2022/2023 hunting seasons.
Table 4. Further comparison of body condition score (BCS) distributions in fallow deer between the 2021/2022 and 2022/2023 hunting seasons.
ConditionMedianTest, p-Value
Excellent2.25Kruskal–Wallis, p = 0.001
Good9.45
Medium14.57
Poorn.a. *
* n.a.: not applicable (no animals were classified as “Poor” in this season).
Table 5. Association between body condition scores and median DON concentrations in fallow deer milk during the 2021/2022 hunting season, Dunn–Bonferroni post hoc test.
Table 5. Association between body condition scores and median DON concentrations in fallow deer milk during the 2021/2022 hunting season, Dunn–Bonferroni post hoc test.
Test StatisticStd. ErrorStd. Test StatisticpAdj. p
Good–Medium−13.255.37−2.470.0140.041
Good–Excellent9.134.542.010.0440.133
Medium–Excellent22.386.073.69<0.0010.001
Adj. p: values adjusted with Bonferroni correction.
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Lakatos, I.; Plank, P.; Tóth, A.; Molnár, Z.; Skoda, G.; Ferenczi, S.; Sükösd, F.; Nagyéri, G.; Szemethy, L.; Szőke, Z. Detection of Mycotoxins in Fallow Deer Milk and Feces: Evidence of Climate-Driven Contamination in a Comparative Study of Two Weather-Divergent Years in Hungary. Toxins 2026, 18, 93. https://doi.org/10.3390/toxins18020093

AMA Style

Lakatos I, Plank P, Tóth A, Molnár Z, Skoda G, Ferenczi S, Sükösd F, Nagyéri G, Szemethy L, Szőke Z. Detection of Mycotoxins in Fallow Deer Milk and Feces: Evidence of Climate-Driven Contamination in a Comparative Study of Two Weather-Divergent Years in Hungary. Toxins. 2026; 18(2):93. https://doi.org/10.3390/toxins18020093

Chicago/Turabian Style

Lakatos, István, Patrik Plank, Arnold Tóth, Zsófia Molnár, Gabriella Skoda, Szilamér Ferenczi, Farkas Sükösd, György Nagyéri, László Szemethy, and Zsuzsanna Szőke. 2026. "Detection of Mycotoxins in Fallow Deer Milk and Feces: Evidence of Climate-Driven Contamination in a Comparative Study of Two Weather-Divergent Years in Hungary" Toxins 18, no. 2: 93. https://doi.org/10.3390/toxins18020093

APA Style

Lakatos, I., Plank, P., Tóth, A., Molnár, Z., Skoda, G., Ferenczi, S., Sükösd, F., Nagyéri, G., Szemethy, L., & Szőke, Z. (2026). Detection of Mycotoxins in Fallow Deer Milk and Feces: Evidence of Climate-Driven Contamination in a Comparative Study of Two Weather-Divergent Years in Hungary. Toxins, 18(2), 93. https://doi.org/10.3390/toxins18020093

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