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https://www.thebrooke.org/for-professionals/brookes-animal-welfare-policy

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jackasses is believed to be portrayed by vast admissions with low supplement extraction. High rates of admission can be accomplished by fast biting, substantial nibbles, successful biting per chomp, or penance in molecule estimate reduction.The jackass varies to some degree from the steed since its limited gag and versatile lips advance more noteworthy selectivity in sustaining. This enables it to expand feed quality as opposed to quantity.The measure of time a jackass spends biting a given measure of nourishment is influenced by an assortment of components, including the physiologic state, dimension of admission, measure of fiber in the eating routine, physical type of the feed, molecule size of the feed and the strength of the animal.

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Young neonatal behavior
1.nursing
2.crawling
3.bonding

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Mother maternal behavior
1. unfriendly females turn to need attention, sociable female turn standoffish
2. bite or kick while being handled
3. whinny

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In the back you see an jennet opening and closing her mouth (tthats what they do in heat) and also positioning herself for the jack.

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Female miniature donkey in heat "chewing"

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You never posted it.

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what I feel is happening in this video is that since the camel is bigger than the donkey, the donkey is showing offensive fighting to get what he wants.

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The jack, with full erection, will move toward an estrus female horse, that is exhibiting typically passive estrus behavior,then the jack begin mounting the horse, cranial (caudal methodology), lastly effective sexual intercourse.

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The animal I will be using this semester.

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Why I choose this livestock

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https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0101877

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Positive: food,scratches,interaction with other animals,being free in the field with their companions
negative:pull on the lead rope or negative body language

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The way that donkeys communicate is by utilizing a wide scope of body language and vocalizations. After greeting, jackasses will frequently blow gently into each other's nose, which gives necessary information. Donkeys cautiously move and position their heads, necks, bodies, and tails so as to give pieces of information as to their attitudes. In a desert situation, donkeys should frequently spread out to discover nourishment. With their uproarious bawls and sharp feeling of hearing, jackasses can connect from miles away. https://www.animalrahat.com/playful-smart-and-social-the-hidden-life-of-donkeys/

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https://www.horsetalk.co.nz/2018/04/06/jackass-genomics-donkeys-arise-inverted-chromosome/

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Summary: This journal was about the study of donkeys. To see if the conduct of working donkey was affected by sexual orientation, the season of day or varied between days with the end goal of surveying how vigorous these measures are for incorporation in a working donkeys ethogram.

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Donkey use the territoriality systems. male jackasses build up their region in two different ways: by dropping their defecation and by braying. males will demonstrate to different males that the females inside their region have a place with them by examining female feces and covering it with their urine.If a subordinate male methodologies the females in estrus, the regional male will pursue the encroaching male away.Dominant jackasses will control assets, for example, water and brushing grounds to guarantee they have elite access to the inhabitant females, however will safeguard these assets against interfering subordinate guys. Prevailing jackasses will bawl to caution the crowd of a predator and will look down and pursue away the predator while whatever is left of the group escapes. In the event that the predator continues seeking after an individual from the group, the jackass will kick,chomp, charge at and stomp an attacker.https://animals.mom.me/how-do-donkeys-show-dominance-12303634.html

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Summary: This Article is about where donkeys come from and how the complementary couples make a new species. Also. Leading into the history of the chromosomal aspects.

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A foal can create poor conduct qualities if hand-raised without other creature contact.when the foal is more than one month of age, it tends to be joined by a pony or donkey. This friend will go about being a role model.https://www.thedonkeysanctuary.org.uk/what-we-do/knowledge-and-advice/for-owners/donkey-foaling

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I don't think sensory biases affect donkeys because they are fast learners. Learning can be portrayed as the picking up of information that prompts generally lasting changes in behaviour.Every time you connect with your donkey they will master something. Amid learning the donkey does not view their conduct as great or awful for the human, yet just whether the conduct is viable for them.http://donkeywelfaresymposium.homestead.com/Understanding_Donkey_Behaviour__Ben_Hart.pdf

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This research shows an operant conditioning

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Department of Genetics, Faculty of Veterinary Sciences, University of Córdoba, 14071 Córdoba, Spain

The Worldwide Donkey Breeds Project, Faculty of Veterinary Sciences, University of Córdoba, 14071 Córdoba, Spain

Departament de Ciència Animal i dels Aliments, Facultat de Veterinària, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain

Author to whom correspondence should be addressed.

cognition; cold wave; learning abilities; lunar phases; meteorological conditions

1. Introduction

The hypothetical conditioning effects of weather, moon and climate oscillations on animal behavior and cognition have been widely but unscientifically reported. Popular knowledge has even provided untested testimony of the possibility to predict short-term future meteorological conditions basing on how animals react to the environment around them. This framework has promoted the appearance of the first empirical studies on the clinical and productive implications of such environmental factors in different animal species.

Great scale migration of animal populations, adaptation, or even census reduction or extinction have become proved symptoms of how life cycles may be affected by this progressively changing environmental situation. However, the alteration of the particular environmental characteristics of specific areas has also been suggested to lead the lower scale evolutionary process of local animal life cycles [ 1].

Research has focused on the study of the climatological alteration of physiological processes such as reproduction, and animal biorhythms in populations of different species [ 2]. By contrast, cognitive or behavioral alterations affecting animal populations may remain unnoticed due to being attributed to other more probable causes.

The study of the effects of factors such as season and weather on animal behavior and mood has typically focused on understanding the changes in the ethological patterns conditioning animal routine and daily activities. These changes may globally appear as a consequence of the evolution of certain areas, which may no longer fulfil the unique set of requirements of the animal populations inhabiting them [ 3].

Parallel to these more or less quantifiable effects, there is also a simultaneous repercussion on animal cognitive or behavioral health [ 4]. These effects may not only alter the components of disorder incidence but may also condition animal physiology, as they increase the levels of sensitivity or even distort the cognitive status of specific populations producing long-lasting consequences.

When we consider these behavioral and cognitive registers under a local specific context, we can trace back their origin up to potential weather or meteorological condition related situation or event [ 5].

Scientists have paid attention to the study of the environmental changes that may distort seasonal and circadian rhythms in different species. However, the effects of factors such as the moon cycle on animal behavior have only been approached assessing the alterations occurring on daily animal patterns or physiological rhythms [ 6]. Not to mention the inexistence of research assessing other traditionally folklore-reported environmental effects on cognition, such as the hypersensibility to anticipate particular events. The role on neuroanatomy, ethology, and endocrinology and the activity and effects of neurohormones releasing cycles may be triggered and regulated by the electromagnetic radiation and the gravitational pull of the moon and light cycles during the different moon phases, which may reflect in psychological processes such as mood or cognitive abilities.

The first aim of this research is to study at which level environmental factors such as season, year, moon cycle, meteorological factors, and climate oscillations may affect the response type and intensity, mood and learning abilities of donkeys. Second, we used categorical principal component analyses (CATPCA) to study the possibility to reduce our set of environmental variables to a smaller set that still contains most of the information in the previous one, hence reducing the likelihood of Type I error that can derive when testing for the effects of a large number of explanatory and predictor variables. Third, using this reduced information, we designed regression equations using categorical regression (CATREG) to explain, trace back, and predict the possible behavioral repercussions that certain environmental situations may have, and how these consequences may alter the behavioral patterns that donkeys display through their lives, in order to provide clues on how behavior can become a useful tool for daily care.

2. Materials and Methods

2.1. Animal Sample

Our study sample comprised 78 Andalusian uncastrated jacks and 222 unneutered jennies (n = 300), born from 1990 to 2012 and officially registered in the national studbook of the Andalusian donkey breed. As the age range was not normally distributed (p < 0.05, Kolmogorov-Smirnov test for normality) we used minimum, Q1, median, Q3 and maximum to describe the age range in our sample. Minimum age in the range was 0.27 months, Q1 age was 29.76 months, median age was 77.04 months, Q3 age was 129.07 months, and the maximum age was 270.40 months.

2.2. Information Registration

The records for each animal consisted of information on 18 categorical variables divided into two sets. The first set of 4 dependent behavioral categorical variables assessed the cognitive performance of donkeys through their response type, response intensity, mood/emotion, and learning ability. The variables in this first set could be conditioned by a second set of independent variables comprising 14 environmental factors. A summary of the variables and categories included in the first variable set is described in Table S1, while Table S2 shows a summary of the factors and categories included in the second categorical factor set. Table S3 shows the descriptive statistics, and numerical parametrization of all the variables analyzed. Table S1 presents Category description and definition for response type, the intensity of response, mood/emotion, and learning variables directly controlled during the operant conditioning test.

2.3. Categorical Behavioral Variables

The reaction developed by the donkeys when they faced the six consecutive treatments provided information on four categorical behavioral variables ( Table S1). To name the mood/emotion variable, we considered the definitions by Cabanac [ 7] and Mendl et al. [ 8]. Table S4 shows a description of the scales used to score the response type and mood/emotion variables. The intensity of response and learning ability variables were subdivided into five categories each described as shown in Table S1. The appraiser scored the animals relying on the intensity of their responses from low intensity responses to high intensity responses whatever the mood/emotion displayed by them was ( Tables S2 and S3). As animals were only scored once, opposite behaviors were not scored correlatively in the same animal. That is to say, the response of an animal displaying a high intensity calm mood/emotion (very calm animal) was not registered as a low intensity nervous mood/emotion (slightly nervous mood/emotion) simultaneously. The reason for this is the fact that an animal cannot be nervous and calm at the same time whatever it is the intensity level at which such animal expresses its mood/emotion status (see Table S4).

2.4. Qualitative Behavioral Assessment

The same trained judge registered each donkey's mood/emotion following the protocols developed by Navas et al. [ 9] which based on Minero et al. [ 10]. Navas et al. [ 9] generated the descriptor lists for the use in subsequent studies as the present one. Table S4 shows a summary of the mood/emotion descriptors used concerning Table 2.

2.5. Noncognitive Categorical Factors

Environmental categorical factors could be divided into two groups. Meteorological and environmental conditions included year of evaluation, the season of evaluation, weather conditions, temperature, moon phase at evaluation, relative humidity, windspeed, sunlight hours, barometric pressure, rainfall on the day of evaluation, and rainfall on the following day. Animal birth characteristics included season of birth, year of birth and moon phase at birth. Table S2 shows the categories for independent noncognitive factors in the second set.

The information was registered during the yearly behavior assessment sessions carried out on four random days per year, from June to November for three consecutive years from 2013 to 2015 at twenty-two different farms all over Andalusia (Southern Spain).

The 22 farms involved, reared their animals under four husbandry systems (extensive, semi extensive, semi intensive and intensive) and were located in 5 Andalusian provinces (Southern Spain). The 6% of the donkeys were tested during the breed's Official Morphological Contest held by the Union of Andalusian Donkey Breeders (UGRA).

2.6. Meteorological and Moon Cycle Records

Day records for temperature, relative humidity, windspeed, sunlight hours, barometric pressure, rainfall per day and rainfall prediction (on the following day) were obtained from the State Meteorological Agency (AEMET) ( http://www.aemet.es/). Moon phase at evaluation and moon phase at birth records were obtained from the Astronomical Applications Department of the US Naval Observatory ( http://aa.usno.navy.mil).

2.7. Operant Conditioning Behavioral Test

The operant conditioning behavioral test was carried out in an open area to which the donkeys were previously accustomed (it was part of the area over which the donkeys developed their daily activities). During the operant conditioning test, the donkeys were made cross over a 200 × 200 cm oilcloth with a wooden print on it using increasingly aversive reinforcement methods (from stimuli 1 to 6). We exposed each animal to six reinforcement treatments consecutively, one at each of the six stages within the operant conditioning test. At each stage, handler A and handler B used each of the six different reinforcement treatments to lead the donkeys to cross over an oilcloth laying on the floor. These treatments/stimuli could comprise unknown elements (the animal had not been familiarized to them) or known elements (to which the animal had already been familiarized). These elements could be visual (elements fell within the visual areas of the donkeys) and/or acoustic (elements generated sounds, i.e., "motivator" or claps, although they may or may not fall within visual areas) and were presented to the donkeys from different positions (from the front or from a rear position always at 2 m away from the animals). A cameraman (Handler C) simultaneously videotaped the experiences (1080 p, 50 Hz, shutter speed: 1/250 s) to assess the donkey's performance after the field experiences and to test for intra-observer discrepancies. Cameraman (Handler C) controlled timing. A detailed description of the operant conditioning test, the reinforcement treatments, stimuli descriptions and classification and their constituting elements are described in Navas et al. [ 9] and Navas González et al. [ 11], and summarized in Table 1 and Table 2.

2.8. Test and Scoring System Reliability

Statistical tests did not report intra-observer discrepancies as all the scores obtained on the field matched those obtained after reviewing the tapes again. Aiming at eliminating the effect of appraiser to reduce the likelihood of subjective evaluations, 50 individuals (16.67% of the total sample) were tested using the operant conditioning test described scoring for the categorical variables of response type, mood and response intensity at a preliminary stage of the study. Cohen's κ determined whether the repeatability of the model was enough to delete the effect of the appraiser from the model, providing a measure of the accuracy of scoring of the appraisers. Then 95% confidence intervals (95% kappa IC) were computed according to 95% kappa IC = κ ± 1.96 SEκ, where; SEκ = [(po(1 − po)/n(1 − pe) ²] 0.5 with the Crosstabs procedure of SPSS Statistics for Windows, Version 24.0, IBM Corp. (2016, Armonk, NY, USA). This preliminary analysis aimed at testing for interobserver reliability, i.e., the reliability of the scoring system, which proved to be highly reliable as there was highly statistically significant perfect agreement between the three appraisers' judgements when scoring for response type and response intensity for the six stimuli/treatments presented. Each stimulus corresponded to one of the six stages in the test ( Table 1). When testing for mood/emotion, there was highly statistically significant almost perfect agreement among the three observers at the preliminary test for repeatability for all the traits and stimuli, except when testing for mood at the presentation of stimulus/treatment 3. In this case, the strength of agreement between appraisers 1 and 2 and 2 and 3 was substantial and at the presentation of stimuli/treatments 1 and 6, for appraisers 2 and 3 between whom inter-observer agreement was substantial. The slight distortion occurring may be attributed to the change in the kind of reinforcement applied to make the donkeys cross over the oilcloth on the floor occurring in stimuli/treatments 1, 3, and 6. At the presentation of stimulus/treatment 1, the animal passed from being at rest to start the operant conditioning test. At the presentation of stimulus/treatment 3, the animals went from being exposed to negative reinforcement (stimulus/treatment 2) to being exposed to positive/neutral reinforcement (stimulus/treatment 3). Finally, at the presentation of stimulus/treatment 6, the stimulus changed from being presented at the visible area of the donkey to be located at a rear position (blind area). Table S5 shows the results for interobserver reliability tests at this preliminary study.

2.9. Statistical Analysis

Categorical variables represent a qualitative method of scoring data. As all the variables and factors considered in our study were categorical, we used nonparametric tests to assess the information recorded statistically. A Chi-square test for independence was used to analyze whether the factors in the second set ( Table S2) randomly and significantly influenced the variables in the first set ( Table S1). Chi square is neutral to the parametric or non-parametric nature of the distribution and is relatively robust to situations with a limited number of data (n > 50). The most appropriate statistic to use as a measure of Chi-square association is Cramér's V. Cramér's V is used to measure the strength of linear correlation, that is to test for the multicollinearity and significance between each variable from the first set with each variable from the second set using the Crosstabs procedure from SPSS Statistics for Windows, Version 24.0, IBM Corp. (2016, Armonk, NY, USA) according to the indications of Nolan [ 17]. Table S6 shows total and relative frequencies for the associations of the four dependent categorical variables with the environmental variables.

Categorical principal components analysis (CATPCA) was used to quantify categorical factors while reducing the dimensionality of the data and Categorical regression to establish the most important descriptive and discriminative noncognitive factors on the variables considered using the Optimal Scaling procedure from the Dimension reduction task from SPSS Statistics for Windows, Version 24.0, IBM Corp. (2016, Armonk, NY, USA). Reducing the dimensionality of relatively large sets of variables prevents type I errors from occurring, as we may strip our model to the core independent variables affecting the dependent variables studied by our model. A lower number of variables means we may need stronger evidence against the null hypothesis H ₀ (via a lower p-value) before we will reject the null. Therefore, if the null hypothesis is true, we will be less likely to reject it by chance. This reduced information was used later at the categorical regression (CATREG) analysis.

We used CATREG to describe regression models to study how the variables assessed depended on the factors considered. The resulting regression equations could be used to trace back, explain, or predict behavior or cognitive abilities for any combination of the 14 independent factors. Categorical regression was carried out using the Optimal Scaling procedure from the Regression task from SPSS Statistics for Windows, Version 24.0, IBM Corp. (2016, Armonk, NY, USA).

2.10. Justification for Statistical Tests

The most appropriate statistic to use as a measure of Chi-square association is Cramér's V. Cramer's V is a measure of association for nominal variables. Effectively it is the Pearson chi-square statistic rescaled to have values between 0 and 1 as follows:

V= χ 2 n o b s(min( n c o l s, n r o w s))−1

where χnnn ² is the Pearson chi-square, obs represents the number of observations included in the table, and where cols and rows are the number of columns and rows in the table, respectively. For a 2 by 2 table, of course, this is just the square root of chi-square divided by the number of observations, which is also known as the phi coefficient. Cramer's V squared is the average of the squares of the canonical correlation coefficient between two categorical variables. Such canonical-correlation analysis will find the strength that linear combinations of the X i and Y j have on each other. When using Cramér's V small effect associations range from 0.0 to 0.10, medium effect associations from 0.3 to 0.5 and large effect associations from 0.5 to anything above. The same author would recommend that the interpretation of effect size should consider a statistically significant measure (p < 0.05) with a small effect size or higher to indicate a meaningful difference, especially for behavioral or psychological studies.

CATPCA is appropriate to reveal the inherent overlapping nature of behavioral variables, hence becomes suitable for variable selection and dimension reduction in categorical variables. This statistical test analyses the interrelationships among a large number of variables and explains these variables regarding their common underlying dimensions. The objective is to find a few linear combinations of the variables (factors) that can be used to summarize the data without losing too much information in the process. CATPCA is a nonparametric method that quantifies categorical variables through a process called optimal scaling. Optimal scaling uses category quantifications in such a way that they account for as much as possible of the variance in the quantified variables. The most relevant characteristic of CATPCA is that it can handle and discover nonlinear relationships between variables. Because CATPCA directly analyses the data matrix and not the derived correlation matrix, so that, we can avoid the usual concern to have at least five times as many observations as the variables. CATPCA suits analysis in which there are more variables than objects. In behavioral sciences many of the variables used are qualitative, nominal or ordinal, thus indicating the use of CATPCA, which has been demonstrated to be more robust than PCA when assessing categorical variables.

CATPCA eigenvalues are indicators of how many dimensions are needed. As a general rule, when all variables are either single nominal, ordinal, or numerical, the eigen value for a dimension should be larger than 1. For multiple nominal variables, there is no easy rule of thumb to determine the appropriate number of dimensions. If we replace the number of variables by the total number of categories minus the number of variables, the above rule still holds. However, this rule alone would probably allow more dimensions than are needed. When choosing the number of dimensions, the most useful guideline is to keep the number small enough so that meaningful interpretations are possible. The model summary table also shows Cronbach's alpha (a measure of reliability), which is maximized by the procedure. In this study, the stepwise method was used to prevent the possible multicollinearity problem that could arise in the linear multiple regression model formed by transformed variables. The resulting reduced set of variables can be used to perform a categorical regression analysis to build significant behavioral descriptive equations that enable quantifying the result of the effects of specific combinations of environmental factors on behavioral variables, such as response type or intensity, mood or learning abilities.

When assessing non-parametrical data, categorical variables can be included as independent variables in a regression analysis but must be converted to quantitative data for us to be able to analyze them. Ordinary linear regression models could only be used when the dependent variable is quantitative and predictive variables are either quantitative or dummy. The analysis of such ordinary linear regression models involves minimizing the sum of squared differences between a response (dependent) variable and a weighted combination of predictors (independent). Variables are typically quantitative, with (nominal) categorical data recoded to binary or contrast variables. As a result, categorical variables serve to separate groups of cases, and the technique estimates separate sets of parameters for each group. The estimated coefficients reflect how changes in the predictors affect the response. Prediction of the response is possible for any combination of predictor values. CATREG extends the standard approach by simultaneously scaling nominal, ordinal, and numerical variables. The procedure quantifies (transforms) categorical variables so that the quantifications reflect characteristics of the original categories. The procedure treats quantified categorical variables in the same way as numerical variables. Using nonlinear transformations allow variables to be analyzed at a variety of levels to find the best-fitting model. R-squared evaluates the scatter of the data points around the fitted regression line. It is also called the coefficient of determination, or the coefficient of multiple determination for multiple regression. For the same data set, higher R-squared values represent smaller differences between the observed data and the fitted values. R-squared is the percentage of the dependent variable variation that a linear model explains. As the independent noncognitive categorical factors registered in our study were categorical and the data was sorted into categories following different criteria, we used standardized coefficients to interpret and compare their effects on our behavioral dependent categorical variables. When we apply a stepwise linear regression model to the transformed variables, the standardized and unstandardized coefficients are equal. Hence, we can interpret the unstandardized coefficients. Standardized coefficients represent regression results with standard scores. By default, most statistical software, like SPSS, automatically converts both criterion (DV) and predictors (IVs) to Z scores and calculates the regression equation to produce standardized coefficients. When most statisticians refer to standardized coefficients, they refer to the equation in which one converts both DV and IVs to Z scores. In a simple model with two factors involved the coefficients for Z scores for each variable (Z'y) may be interested as follows:

β ₁ mean a standard deviation increase in Z X1 is predicted to result in a β ₁ standard deviation increase in Z'y holding constant Z X2.

β ₂ mean a standard deviation increase in Z X2 is predicted to result in a β ₂ standard deviation increase in Z'y holding constant Z X1.

Therefore, the standardized partial coefficient represents the amount of change in Zy for a standard deviation change in Z X. So, if X1, one factor involved, were increased by one standard deviation, then one would anticipate a β ₁ standard deviation increase in the variable tested holding constant the effect of X2 and vice versa.

With Z X1 and Z X2, being the Z scores for each factor, and β ₁ and β ₂ the standard coefficients for each of the, respectively.

As the above example shows, conversion of raw scores to Z scores changes the unit of measure for interpretation, the change from raw score units to standard deviation units.

As a rule, we assume standardized results reported used full standardization (both DV and IVs were converted to standard scores), and that the Z formula was used for standardization. The general standardized regression equation may follow the following model Z'y = β ₁Z X1 + β ₂Z X2 + ..., where Z'y is the predicted value of Y in Z scores; β ₁ represents the standardized partial regression coefficient for X1; β ₂ represents the standardized partial regression coefficient for X2; and Z X1 and Z X2 are the Z score values for the variables X1 and X2, respectively.

The intercept will always equal 0.00 when standardization is based upon Z scores, and both DV and IVs are standardized.

Once the regression equation is standardized, then the partial effect of a given X upon Y, or Z X upon Zy, becomes somewhat easier to interpret because interpretation is in sd units for all predictors.

3. Results

3.1. Noncognitive Factor Analysis

Table 3 shows the results from Chi-Square and Cramér's V, testing for the existence of linear correlations. Cramér's V effectively measured the strength of collinearity that the noncognitive factors considered have on the behavioral variables studied, given the high significance (p < 0.001) that they report for all the factor-variable combinations except for season at birth and response type ( Table 3). CATREG was performed to the 14 qualitative independent variables (environmental factors) with the four behavioral categorical variables (response type, mood/emotion, the intensity of response and learning ability) as dependent variables. Then stepwise linear regression to the data with the resulted quantifications was applied, and Table 4 and Table 5 present the summary results with the significant variables. Table 5 lists the standardized coefficients (β). CATREG reported all of the independent variables except for season at evaluation to be significant for response type ( Table S7). Season at evaluation and the rainfall on that day were nonsignificant for mood/emotion. Weather conditions, temperature, and barometric pressure were nonsignificant for response intensity and learning ability.

Year of evaluation reported Cramér's V values ranging from 0.146 to 0.267 and CATREG standardized coefficients ranging from 0.065 to 0.242 for the behavioral variables studied ( Table 3, Table 5 and Table S8). The results of CATPCA loadings were 0.017 to 0.700 for dimensions 4 and 2, respectively. These loadings suggested a low to strong direct correlation of this factor ( Table 5 and Table S8).

The range of Cramér's V for moon phase at evaluation for the four variables tested was narrower than the one for other factors (0.102 to 0.121). CATREG standardized coefficient range was narrow as well, ranging from 0.107 to 0.145. Values for the loadings in the CATPCA were negative and low to moderately high for dimensions 1 and 2 (weak to moderate inverse correlation), and positive and moderate to high for dimensions 3 and 4 (moderate to strong direct correlation), respectively. However, moon phase at birth reported a wider range for Cramér's V values than other factors (from 0.111 to 0.388). By contrast, CATREG standardized coefficient range was narrow, ranging from 0.093 to 0.117. Values for the loadings in the CATPCA were positive and from low to moderate (weak to moderate direct correlation) for all the dimensions except for dimension 3, for which the value was negative and moderate (moderate inverse correlation).

Relative humidity Cramér's V ranged from 0.117 to 0.226 for response type and mood/emotion, respectively. CATREG standardized coefficients (β) for relative humidity factor ranged from 0.106 to 0.263 for response intensity and learning, and mood/emotion, respectively. CATPCA loadings were negative and moderately high for dimensions 1 and 3 (moderately strong inverse correlation), and positive and moderate to high for dimensions 2 and 4, addressing a moderate to strong direct correlation.

A categorical principal components analysis (CATPCA) was applied on the total data set of 14 environmental factors with the aim of establishing and interpreting the factors determining the four behavioral variables tested (response type, mood/emotion, intensity of response, and learning) to evaluate for redundancies among them. Two, three, and four-dimensional model results are shown in Table 6. Table 7 shows the factors affecting the four behavioral variables in order of importance according to the CATREG standardized coefficients (β). Since we used the stepwise method, there was no multicollinearity problem. Only 8 of the environmental factors studied contributed to the two-dimensional model in a meaningful way 11 of them meaningfully contributed to the three-dimensional model and 12 of them meaningfully contributed to the four-dimensional model (factor loadings > 0.5, Table 6), then the different components (PC1, PC2, PC3, and PC4) were best described by the factors highlighted in bold in Table 7.

The two-dimensional model has an internal consistency coefficient (Cronbach's Alpha) of 0.880 and yields an eigen value of 5.471 for the first component, indicating that 39.075% of the variance is accounted by this component ( Table 6). For the second component, the internal consistency coefficient is 0.602 with an eigen value of 2.269, indicating that its proportion of variance is 16.204%. On the whole, the internal consistency coefficient (Cronbach's Alpha) for the bi-dimensional model was 0.938, and the eigen value yielded of 7.739, explaining a total of 55.279% of the variability.

Table 6 shows the internal consistency coefficients (Cronbach's Alpha), eigenvalues and percentage of variability explained by each of the components of the three and four-dimensional models. On the whole, the internal consistency coefficient (Cronbach's Alpha) for the three and four-dimensional models were 0.961 and 0.976, respectively. The eigen value yielded for the three and four-dimensional models were of 9.301 and 10.627, respectively, and they explained a total of 66.435% and 75.910% of the variability, respectively.

3.2. Model and Operant Conditioning Test Behavioral Variability Explanatory Quality

CATREG R squared coefficient obtained ranged from 0.288 to 0.375 for the response type, and response intensity and learning ability variables, respectively ( Table 4). In the same way, when CATPCA was implemented, four and three-dimensional models accounted for 75.910% and 66.435% of the total variance of behavioral variables, respectively. These results could compare to those obtained by CATREG. These findings address the fact that two of the components of the study could be summarized into one, with a low loss (9.475%) in the explanatory power of the variability. This low loss could stem from the fact that the response type variable was obtained classifying the levels in the mood/emotion variable, so that response type variable somehow derived from the mood/emotion variable. This percentage of loss is around the same value shown by CATPCA for the explanatory power of the 4th dimension (11.280%).

4. Discussion

Our statistical outputs suggest that the operant conditioning tests and model designed and used for our study efficiently and successfully enable quantifying the variation in the adaptive and cognitive behavioral response of donkeys ( Table 4 and Table 7).

Cramer's V has been stated to be the most suitable parameter for assessing factor strength and testing for significance after the results of cross-sectional studies relying on chi-square analyses. Although most meteorological or climatological variables could be assumed to be approximately normally distributed, some other such as rainfall, remarkably deviate from a Gaussian distribution [ 18]. Chi-square tests become then especially relevant, as they are neutral to the parametric or non-parametric nature of the distribution and relatively robust to situations in which there are only a limited number of data common to endangered populations, as it would be the case of donkey breeds.

As our results suggest, when we aim at comparing continuous environmental factors relying on linear scales with accurately described behavioral or cognitive categorical variables, it is useful to homogenize their nature, turning continuous variables into categorical ones. This homogenization may simplify establishing effective, easily-understandable relationships.

According to Cohen [ 19], when using Cramer's V, small effect associations may range from 0.0 to 0.10, medium effect associations from 0.3 to 0.5 and large effect associations from 0.5 to anything above. The same author would suggest this parameter to be especially suitable for behavioral or psychological studies, considering a statistically significant measure of p < 0.05 with a smaller or greater effect size to indicate a meaningful difference among the categories of a particular factor influencing the different categorical levels of the variables under study.

While studying our first hypothesis, Chi-Square and Cramér's V highlighted there was a significant linear correlation between environmental factors and variables ( Table 3), although the behavioral variables tested were not dependent on some of them as shown in the result section.

Chi-Square and Cramér's V highlighted there was a highly statistically significant linear correlation (p < 0.001) between all environmental factors and variables, except for season at birth which was just significant (p < 0.05) for response intensity and learning ability and non-significant for response type ( Table 3). However, the only factor behavioral variables tested were not dependent on some of them as shown in the result section.

Date of birth has been extensively reported to influence behavior and cognitive abilities in animal models which have later been applied to humans [ 20, 21] with an underneath basis relying on circadian rhythms [ 22], frequently or exclusively focusing on the influence of birth months. However, the CATREG standardized coefficients and CATPCA component loadings reported found an almost three times lower variation and therefore a weaker factor strength for the birth season when compared to birth year. This low variation among seasons could rely on season shifting, one of the most widely discussed events of climate change [ 23]. The occurrence of shifting seasons is directly linked to warmer worldwide temperatures. According to Stine et al. [ 24], the amplitude component of the annual cycle (half the difference between summer and winter temperatures) has progressively decreased in most continental areas. This situation translates into the occurrence of warmer winters resulting in a lower seasonal weather variation through the year, as our results suggest. In the same way, the greater importance and higher relative frequency for birth year variations may support all of the long-term progressively increasing temperature records existing from one year to another since 1884 [ 25].

It may be worth noting that the late gestation of the animals displaying a depressive behavior pattern took place during the winter to early spring of 2005, when the cold wave accounting for the lowest temperature in the last 117 years, took place in Spain [ 26]. This situation may be worsened given the characteristics of the light grey coat of Andalusian donkeys which makes them more sensitive to cold weather. Furthermore, the animals born during that spring were all jennies. Studies in humans [ 27] and rats [ 28] have reported that the pregnancies of mothers who had been exposed to extreme weather conditions not only presented a resulting offspring with a lower weight at birth and at increased risk to experience developmental, learning, and emotional disorders, but also an altered sex ratio, lowering the occurrence of newborn male offspring in different species [ 29, 30, 31].

Moon phases have been reported to increase the number of deliveries in cows [ 32]. The same authors would report that apart from the higher birth rates of the dairy cows near and during the full moon, the predicted and real delivery dates significantly differed within the eight moon phases. Cows with predicted delivery dates before the first-quarter moon tended to deliver later than expected, whereas cows with delivery dates on a full moon to last-quarter phase tended to deliver on schedule. Although our study is the first to attempt the assessment of the effect of the moon phase at birth on mood or behavior, it is possible that this reported alteration on the times at delivery may be the basis for different degree alterations of cognitive development. These cognitive alterations may translate into future behavioral mood statuses, as suggested by the near 10% linearly correlated effect of moon phase at birth on learning abilities and 12% linearly correlated effect of moon phase at birth on mood found in our study through Cramér's V and CATREG. Figure 1 shows the relative frequency distribution for different mood/emotion patterns displayed by the donkeys relative to the phase of the moon at the moment of birth and at the time of evaluation.

Our results support the information found by Zakari et al. [ 33], according to which the behavioral repertoire of donkeys is modulated depending on the season. This seasonal evaluation effect has also been reported by equid welfare organizations such as The Brooke in working donkeys [ 34]. The study by Meyer et al. [ 35] in humans reported cognitive abilities to be distorted by a seasonal effect linked to serotonin levels in humans with better cognitive performance in summer, what extended to our experience could explain the increased frequency of animals refusing to cross the unknown surface. Donkeys' increased cognitive abilities have been mistaken with stubbornness. Therefore, refusal to cross new surfaces may be related with an increased ability to assess potentially harmful or dangerous situations.

Moon phase has been reported to alter both humans and animal at many different psychological and physiological levels [ 6]. A slight decrease in the strength of the effect of moon phase at the date of evaluation of more than half the strength for the effect of moon phase at birth was reported according to CATREG standardized coefficients. Cramér's V for moon phase at evaluation was around half the value for moon phase at birth, what suggested a stronger linear correlation between this factor and mood, response intensity, and learning ability variables. The power that the moon exerts on living beings may be mainly attributed to two factors or primary forces which differ along the consecution of the moon cycle; gravity and light changes, and their suggested effect on hormonal production and regulation. Folklore has reported a possibly calmer, hyporeactive status and low cognitive abilities in marine animals like the whale shark, which, as South Sea Islanders believe, are most easily caught a few days after a full moon. In the same way, the Miskito Indians of Eastern Nicaragua, believe that all animals respond to tides, that the woodpecker pecks when the tide is changing, and that hunting and fishing are best at the rising tide, but not at a new moon [ 36]. This has also been reported for hunting behavior in such large felines as lions, which were prone to hunt larger preys during new moon phases [ 37]. The time between two successive high or low tides is 12.4. A "lunar day" is 24.8 h. Tides are greatest at a new moon when the gravitational pull of the sun and moon are both acting in the same direction. Because the moon is moving relative to the Earth and the Sun, "lunar days" are not precisely 24 h [ 38], which at the same time alters normal light cycles. LeGates et al. [ 39] reported that when subjected to an abnormal light cycle, mice's cognitive and mood functions were directly affected through intrinsically photosensitive retinal ganglion cells, which may support the strength of the effects obtained for all variables in our study. The effect of the number of sunlight hours found in our study not only was the stronger one according to CATREG standardized coefficients but also the one holding the strongest inverse correlations for all the dimensions in the CATPCA. Exposure to unnatural lighting can induce significant changes in affection, increasing depressive-like and decreasing anxiety-like responses as it disrupts circadian rhythms of locomotor activity, body temperature, hormones, and the sleep-wake cycle in animals [ 40].

Behavioral responses and mood have been reported to be altered because of weather conditions and the effects of high and low extreme temperature and relative humidity, although still no previous study assessing the direct correlation with weather conditions or environment temperature has been carried out. The results by Denissen et al. [ 41] revealed the main effects of temperature, wind power, and sunlight on negative emotion patterns in humans and this could be extrapolated to donkeys as highlighted by the CATPCA loadings and CATREG standardized coefficients observed for the temperature, relative humidity and weather conditions on the four variables tested ( Table 3, Table 5, Table 8 and Table S8). The basis for this behavioral and possibly cognitive repercussion could be, as stated by [ 42], the fact that endothermic animals such as equids usually keep their body temperature within narrow limits with changing environmental conditions in an attempt to cool brain temperature. This advantage means a drawback as well, as it occurs at a high energetic cost, making endothermic animals face a two-fold challenge. This double challenge could be one of the reasons, as reported by Janczarek et al. [ 43], for adverse changes in the behavior of recreational horses that can occur if the horse is ridden when the air temperature is above 26 °C. These conditions may cause an alteration in mood, with donkeys showing more elusive and hyporeactive responses, and a reduction in the willingness to work in horses and other equids. In our study, this was supported by the increase on the refusal to cross and lack of cooperation when completing the problem-solving test, a decrease on the frequency for neutral responses and an increase in the frequencies for rejective and fearful attitudes when temperature ranged from 25 to 29 °C.

Relative humidity has been reported to be a thermally stressing factor from a welfare perspective and to affect donkey behavior and performance when it reaches extreme upper values as reported by Zakari et al. [ 5] and Gebresenbet et al. [ 44]. Heat loss mechanisms include evaporation, skin blood flow, and cardiovascular support for thermoregulation and exercise. Low temperatures have been reported to inhibit sweat gland in the donkey [ 45] and when simultaneously relative humidity is high this effects increase. Sweat does not readily evaporate from the body, and therefore it cannot reduce its temperature efficiently. When this rate is low, such evaporation rate is excessive therefore causing mucosa and skin dryness and increasing heart rate [ 44]. This situation alters performance in working donkeys and has been reported to reduce complex cognitive capacities in humans [ 46]. Parallelly, the low cooperative response frequency may be attributed to the fact that as temperature increases and relative humidity decreases, when kept around an optimal point for donkeys, they may be prone to display natural behaviors. Donkeys are energetic natural savers [ 47], and they will tend to slow moving and decrease their behavioral activity rather than display the compensative methods that they are likely to present under stressing meteorological situations [ 5].

Extreme high windspeed has been reported to be a welfare distorting factor for donkeys [ 5, 48] to which individuals may adapt differently. Interestingly, as windspeed decreased, the responses of the donkeys became milder, and their attitudes turn less cooperative. White or light coat animals such as the Andalusian donkey have been reported to absorb more heat under higher to 3 m/s windspeeds, which may make them develop more stressful responses [ 49], hence, the high frequency for stress related moods and slightly lower intensity responses for calmer or cooperative moods. The low variation found, may account for the similar values obtained for almost all the variables. Similarly to our findings, studies in mice have reported a pronounced behavioral inhibition as well as a cognitive disruption because of an increase in the duration of light phases per day, which should be considered when testing animals for such traits [ 50].

Slight barometric pressure fluctuations have traditionally been reported to promote behavioral and feeding activity in fish. Fishers usually relate slight changes towards high pressure to clear sky occurrence during which fishing is medium to slow as fish may slowly be in deeper water or near cover. These trends progressively invert when there is falling pressure, the best attributed timing for fishing during degrading weather when fish are more active what may support our results [ 51], though still no previous scientific research has been carried out on the effects of slight variations on barometric pressure. Studies on rats have reported individuals to be more prone to develop depressive behavioral patterns when they are exposed to a sharp fall in barometric pressure (20 hPa below the natural atmospheric pressure) [ 52]. However, the animals in our study were not exposed to such extreme air pressure variations.

Rainfall has been reported to be especially crucial as a welfare distorting or stressful provoking factor in donkeys [ 48]. Curiously, donkeys have traditionally been attributed the ability to predict lousy weather (Graphical abstract) and rain occurrence [ 53, 54] as it could be stated by this study, although this may be the first attempt to scientifically proof such ability.

5. Conclusions

Environmental conditions, seasonal, timing (year) and moon cycle phases are potential stress factors or behavioral modulators that affect the behavior and cognitive responses of donkeys, as well as may have potentially long lasting effects which can be traced back. Climate oscillation effects may affect donkeys altering their physiological biorhythms and produce severe behavioral and cognitive modifications. Deviations in behavioral patterns or on the abilities of the donkeys to perform complex tasks to which they may not be accustomed may become relevant indicators of welfare as well as they may address the most suitable techniques or methods to be applied in each case. Furthermore, behavior becomes a relevant tool when predicting future weather conditions as well as may report the potential distortion that they may cause, a prominent importance fact for veterinarians, practitioners and donkey owners, as it may allow them to anticipate such situations in order to counteract their effects.

Supplementary Materials

The following are available online at https://www.mdpi.com/2076-2615/8/11/215/s1. Table S1. Category description and definition for response type, the intensity of response, mood/emotion, and learning variables directly controlled during the operant conditioning test; Table S2. Categorical variable description and levels for the effects of meteorological environment and birth characteristics collaterally controlled during the fulfilment of the test during the first phase of the study; Table S3. Descriptive statistics and numerical parametrization of all the variables analyzed; Table S4. Description for the mood and response type behavioral categorical variables and "Mercalli" scales; Table S5. Cohen's kappa and 95% confidence interval for inter-observer reliability testing; Table S6. Total and relative frequencies for the associations of the four dependent categorical variables (type and intensity/degree of response, mood/attitude and problem-solving success/learning rate) with eleven independent environmental factors (year, season and moon phase at evaluation, temperature, relative humidity, windspeed, sunlight hours, barometric pressure, rainfall per day, rainfall on the following day and weather conditions); and the three birth related environmental characteristics (season, year and moon phase at birth).; Table S7. CATREG Standardized Coefficients (β) sorted in order of importance on the variables tested; Table S8. Cramér's V (Chi squared), Standardized Coefficients (β) (CATREG) and loadings (CATPCA) output comparison.

Author Contributions

Conceptualization, F.J.N.G.; Data curation, F.J.N.G., J.J.V. and J.V.D.B.; Formal analysis, F.J.N.G., J.J.V. and J.V.D.B.; Investigation, F.J.N.G., G.P.I. and A.A.A.; Methodology, F.J.N.G., J.J.V. and J.V.D.B.; Resources, F.J.N.G., G.P.I. and A.A.A.; Software, F.J.N.G.; Supervision, J.J.V. and J.V.D.B.; Validation, J.V.D.B.; Writing-original draft, F.J.N.G.; Writing-review & editing, F.J.N.G., J.J.V. and J.V.D.B.

Funding

This research received no external funding.

Acknowledgments

This work would not have been possible if it had not been for the support and assistance of UGRA (Union of Breeders of the Andalusian Donkey Breed) and the PAIDI AGR 218 research group. Especially we would like to thank the international team composed of Navas B., McLean A., and León, J.M.

Conflicts of Interest

The authors declare no conflict of interest.

Ethics Statement

All farms included in the study followed specific codes of good practices for equids and particularly donkeys and therefore, the animals received humane care in compliance with the national guide for the care and use of laboratory and farm animals in research. All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki. The Spanish Ministry of Economy and Competitivity through the Royal Decree Law 53/2013 and its credited entity, the Ethics Committee of Animal Experimentation from the University of Córdoba, permitted the application of the protocols present in this study as cited in the 5th section of its 2nd article, as the animals assessed were used for credited zootechnical use. This national Decree follows the European Union Directive 2010/63/UE, from the 22 September of 2010.

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Test Factors

Descriptions

Time per stage/treatment presentation

75 s per stage/treatment presentation. The application of the reinforcement treatments that Handler A, Handler B or both implemented to lead the donkey across the oilcloth lasted for the whole 75 s. These treatments were applied to check the response of the animals to the different types of reinforcement. No additional time was supplied for the donkeys to complete the stages, so that, once the 75 s, provided to the donkeys to interact with the elements presented, had expired, the following stage started and the next treatment was implemented.

Test duration

450 s.

Test stages

1 to 6. Each stage corresponded to the implementation of each of the six reinforcement treatments.

Previous considerations

The oilcloth was the element (obstacle) that the donkeys were led to cross over. No donkey had been in contact with the oilcloth previous to the test. Handlers A and B, used 6 reinforcement treatments to lead the donkeys cross over such obstacle.

The donkeys were accustomed to the area in which the test took place as it was an open area on which the donkeys used to carry out their daily activities.

The donkeys that were taking the test were not present while the oilcloth was being laid on the floor for the first time. The donkeys were assessed one at a time, so no additional donkey was present while the test was taking place.

The test started when Handler B raised the oilcloth and relayed it again on the floor in front of the donkey being tested. This action only took place 1 minute before stage 1 (before the 1st treatment was implemented) and was not repeated further in the test. Cameraman started controlling time after the oilcloth had been relayed, when Handler A gave the first step forward towards the oilcloth.

Frontal and visual elements fell within the visual scope of the donkeys, while we considered rear elements those that fell into a blind area. Acoustic elements could be frontal or rear and emitted sounds.

Reinforcement treatments comprised different elements. Known elements were those which had already been presented to the donkeys at any point in their lives (relying on owner's information), while unknown elements were those to which, according to the owner, the donkeys were not acquainted.

All the reinforcement treatments were implemented sequentially and consecutively from stage 1 to 6, one after another, without any stop between each of them, whether the donkey had completed each stage (crossed the obstacle) completely or not (avoided it). That is to say, the fact that an animal crossed/avoided the oilcloth completely in one of the treatments from 1 to 6, did not prevent the rest of treatments from being implemented.

Legend

Donkey being tested.

Test Stage Descriptions Test Stage Descriptions

Oilcloth presented to the donkey for the first time (Frontal unknown element).

The donkey is given 75 s to complete Stage 1, that is to cross over the oilcloth.

Using a lead rope and soft voice, Handler A tried to comfort the donkey to make it cross the oilcloth on the floor, but without pulling from the rope if the donkey refused to move (Neutral reinforcement).

Treatment/Stimulus

Stimulus Description

Stimulus Type

Reinforcement

Treatment 1 (S1): Soft voice

Handler (B) uses a lead rope and soft voice, trying to comfort the donkey to make the donkey cross the oilcloth on the floor, but without pulling the rope if the donkey refuses to move.

Unknown frontal visual stimulus.

Neutral a

Treatment 2 (S2): Pressure to leading rope

Handler (B) uses a lead rope with applied pressure to make the donkey cross over the oilcloth. Handler (B) releases the pressure when the donkey moves as it crosses the oilcloth.

Known frontal visual stimulus.

Negative b

Treatment 3 (S3): Treat

A familiar treat is used to lure the donkey (dry bread, carrots or feed, depending on the owner's tastes and to which the donkeys on each farm were accustomed). We use the treat that the owner regularly uses as a treat for all of the donkeys in the same farm (the attraction or attention of the animals to the treats depends on whether they are used to the treats presented or not as empirical observations had revealed at a preliminary stage when developing the operant conditioning test). When the donkeys are not familiar to the treats presented, they do not respond to the stimulus by handler (C). The treat is given to the donkey once the task is completed.

Known frontal visual stimulus.

Positive/Luring c

Treatment 4 (S4): Motivator

Handler (B) applies pressure to the lead rope, and handler C makes noise from behind the donkey with a so-called "donkey motivator" (plastic bag tied on the end of a stick) [ 12]. Handler (B) leads the donkey by slightly pulling the rope until the donkey crosses the oilcloth completely.

A known frontal visual stimulus and an unknown rear auditory stimulus.

Negative

Treatment 5 (S5): Double rope leading

Two handlers (B and C) using two lead ropes attached on either side of the halter to encourage the donkey across. The handlers (B and C) release the pressure when the donkey moves and then reapply the pressure when it stops until the donkey crosses the oilcloth completely.

Known frontal visual stimulus.

Negative

Treatment 6 (S6): Clapping

Handler (B) applies pressure on the lead rope, and handler (C) encourages the donkeys across by an acoustic sound. Handler C claps their hands from behind the donkey to make it move forward [ 13]. Pressure and sound are released or stopped when the donkey moves and reapplied when it stops until the donkey had completed the task.

A known frontal visual stimulus and an unknown rear acoustic stimulus.

Negative

Variable

N

Response Type

Mood/Emotion

Response Intensity

Learning Ability

χ²

p-Value

Cramer's V

χ²

p-Value

Cramer's V

χ²

p-Value

Cramer's V

χ²

p-Value

Cramer's V

Environmental/Meteorological

Animal Birth

Variable

R

R Square

Adjusted R Square

Significance

Variable

Response Type

Mood/Emotion

Response Intensity

Learning Ability

β

Sig.

β

Sig.

β

Sig.

β

Sig.

Dimension

Cronbach's Alpha

Total (Eigenvalue)

% of Variance

Dimension

Cronbach's Alpha

Total (Eigenvalue)

% of Variance

Dimension

Cronbach's Alpha

Total (Eigenvalue)

% of Variance

Total

0.976 a

10.627

75.910

Total

0.961 a

9.301

66.435

Total

0.938 a

7.739

55.279

Environmental Factors

Dimension

Environmental Factors

Dimension

Environmental Factors

Dimension

1

2

1

2

3

1

2

3

4

Barometric pressure
0.666
−0.372

Barometric pressure
0.651
−0.349

−0.220

Year of evaluation

0.183
0.700 0.601
0.017

Temperature

−0.448

−0.206

Temperature

−0.437

−0.350

0.377

Windspeed

−0.405
0.694
−0.33

0.476

Windspeed

−0.344
0.871
Windspeed

−0.336
0.810
0.342

Relative humidity

−0.489
0.660
−0.312

0.453

Relative humidity

−0.404
0.846
Relative humidity

−0.474
0.738
0.353

Temperature

0.274
0.610
0.329

0.035

Year of birth

−0.331

−0.363

Moon phase at birth

0.068

0.375

−0.046

Weather conditions

−0.141

0.291
0.634
−0.198

Moon phase at birth

0.070

0.360

Year of birth

0.075

−0.436
0.659
Moon phase at evaluation

−0.075

−0.324

0.323
0.659

Moon phase at evaluation

−0.180

−0.220

Moon phase at evaluation

−0.008

−0.336
0.589
Year of birth

0.001

−0.329

0.392
0.622

Weather conditions

−0.173

−0.179

Weather conditions

−0.189

−0.095
−0.576
Moon phase at birth

0.002

−0.314

0.23

0.362

Model

Regression Equation

Legend

# data[].attributes.attachment_link.title
Can Donkey Behavior and Cognition Be Used to Trace Back, Explain, or Forecast Moon Cycle and Weather Events?

# data[].attributes.attachment_link.description
Donkeys have been reported to be highly sensitive to environmental changes. Their 8900-8400-year-old evolution process made them interact with diverse environmental situations that were very distant from their harsh origins. These changing situations not only affect donkeys' short-term behavior but may also determine their long-term cognitive skills from birth.