Estimation of breed contributions to present and future genetic diversity of 44 North Eurasian cattle breeds using core set diversity measures

Genet. Sel. Evol. 38 (2006) 201–220 201 c INRA, EDP Sciences, 2006 DOI: 10.1051/gse:2005036 Original article Estimation of breed contributions to present and future genetic diversity of 44 North Eurasian cattle breeds using core set diversity measures Jörn Bennewitz∗a, Juha Kantanenb,IlmaTapiob, Meng Hua Lib,ErnstKalma, Johanna Vilkkib, Innokentyi Ammosovc,ZoyaIva n ova d, Tatyana Kiselyovae, Ruslan Popovf,TheoH.E.Meuwisseng aInstitute of Animal Breeding and Husbandry, Christian-Albrechts-University, 24098 Kiel, Germany bBiotechnology and Food Research, MTT Agrifood Research Finland, 31600 Jokioinen, Finland cBatagay-Alyta, 678580, Sakha, Russia dYakut State Agricultural Academy, Ul. Krasilnikova 15, Yakutsk, 677002, Sakha, Russia eDepartment of Genetics and Biotechnology, All-Russian Research Institute for Farm Animal Genetics and Breeding, Moskowskoye shosse–55a, 189620 St. Petersburg-Pushkin, Russia fDepartment of Farm Animals and Breeding, Ministry of Agriculture and Resources of Sakha, Yakutsk, 677007, Sakha, Russia gInstitute of Animal and Aquacultural Sciences, Agriculture University of Norway, Box 5052, 1432 Ås, Norway (Received 13 June 2005; accepted 26 October 2005) Abstract – Extinction of breeds threatens genetic diversity of livestock species. The need to conserve genetic diversity is widely accepted but involves in general two questions: (i) is the expected loss of diversity in a set of breeds within a defined future time horizon large enough to establish a conservation plan, and if so (ii) which breeds should be prioritised for such a conservation plan? The present study uses a marker assisted methodology to address these questions. The methodology combines core set diversity measures with a stochastic method for the estimation of expected future diversity and breed marginal diversities. The latter is defined as the change in the total diversity of all breeds caused by a one unit decrease in extinction probability of a particular breed. The stochastic method was validated by means of simulations. A large field data set consisting of 44 North Eurasian cattle breeds was analysed using simplified determined extinction probabilities. The results show that the expected loss of diversity in this set within the next 20 to 50 years is between 1 and 3% of the actual diversity, provided that ∗Corresponding author: [email protected] Article published by EDP Sciences and available at http://www.edpsciences.org/gse or http://dx.doi.org/10.1051/gse:2005036 202 J. Bennewitz et al. the extinction probabilities which were used are approximately valid. If this loss is to be reduced, it is sufficient to include those three to five breeds with the highest marginal diversity in a conservation scheme. diversity measure /marginal diversity /extinction probability /cattle breeds /genetic conservation 1. INTRODUCTION Extinction of endangered farm animal breeds leads to an irreversible loss of genetic diversity. According to the FAO [8], around one third of the recorded livestock breeds are classified as having a high risk of extinction and around 1000 have vanished during the last 100 years. The need to conserve genetic diversity is widely accepted for biological, economic and cultural reasons [13]. A main reason is that an abundant resource of genetic diversity within each livestock species is the prerequisite of coping with putative future changes in livestock farming conditions. Because financial funds available for conservation of diversity are limited, it is in general only possible to conserve a subset of important breeds rather than all endangered breeds. However, for any investigation regarding genetic diversity within a set of breeds and subsequently for the assessment of importance of particular breeds for diversity, a suitable diversity measure has to be applied. Weitzman [18, 20] described nice mathematical and biological properties of a suitable diversity measure (the so-called Weitzman criteria) and developed a diversity measure that fulfilled these criteria. However, the Weitzman diversity measure was developed to assess diversity across species but is inappropriate across breeds [4,5]. Alternatively, Eding et al. [7] introduced a core set that is built by relative breed contributions in order to maximise genetic diversity within the core set. In their approach, diversity is defined as the genetic variance that can be found in putative offspring that are obtained from interbreeding of those breeds that contribute to the core set [7]. A similar approach was developed by Caballero and Toro [4]. A drawback of this approach might be that it gives no particular weight to the between breed variance, i.e. to the special allele and genotype combinations that are present within breeds. Therefore, an alternative core set was recently introduced by Bennewitz and Meuwissen [2]. Their core set algorithm estimates relative breed contributions in order to maximise total genetic variance that can be found within and between breeds. Both core sets agree with the Weitzman criteria for a proper diversity measure [2,7] and additionally they are less computationally demanding even if a large number of breeds is included in the experiment. Breed contributions to present and future diversity 203 For quantification of expected future diversity and hence of the expected loss of diversity, extinction probabilities for a defined time horizon have to be taken into account. Given that extinction probabilities are known (in real life their estimation is not a trivial task, see [3, 12, 15]), Simianer et al. [17] presented a deterministic method for the simultaneous calculation of expected future diversity and of marginal diversities of the breeds. The latter one is defined as the change in total diversity of all breeds caused by a one unit decrease in extinction probability of a particular breed by a conservation effort [17]. However, the deterministic approach involves 2Ntimes the computation of the diversity algorithm, where Nis the number of breeds included in the experiment. This exponential increase in computation effort limits the application of this algorithm to smaller data sets. This is an even greater problem when the Weitzman diversity measure is used because the Weitzman diversity algorithm is itself computationally very demanding if many breeds are included [18]. This study introduces a stochastic method for the simultaneous estimation of expected future diversity and marginal diversities that is tailored to large data sets. The method was validated by means of simulations and was applied to a large field data set consisting of 44 North Eurasian cattle breeds using the two core set diversity measures mentioned above. The results (i) demonstrated the usefulness of the stochastic method and (ii) of the core set genetic diversity measures for the marker assisted estimation of present and expected future genetic diversity and (iii) they help to identify the most important breeds for the conservation of diversity within this set of North Eurasian cattle breeds, provided that the assigned extinction probabilities are approximately valid. 2. MATERIALS AND METHODS 2.1. Expected future diversity and marginal diversities Assume a set of Nbreeds with known extinction probabilities zfor a defined time horizon t. Further assume that the genetic diversity Dof this set is estimated and the applied diversity measure fulfils the following Weitzman criteria: Monotonicity in species (Dshould not increase when a population is removed) and twin property (addition of a breed that is a copy of a breed already present in the set should not change D). For the estimation of expected future diversity and of breed marginal diversities, the following sampling algorithm can be applied. The algorithm repeatedly generates a sample sfrom the breeds included in the set. It starts with the filling in of an indicator vector k of dimension N(N=number of breeds). Each element kiin kis allocated for 204 J. Bennewitz et al. one breed i, it is either set to zero with an extinction probability zi(breed iis extinct at time t) or to one with a probability 1 – zi(breed iis alive at time t). The breeds with ki=0 are removed from the current sample sand the diversity estimation algorithm is applied to this sample. The estimated diversity within the sample, Ds, is recorded. The algorithm is repeated Stimes (i.e. S different samples s). The expected diversity at the end of the defined time horizon tcan be estimated as: E(Dt)=1 S S  s=1 Ds,(1) and the variance of the expected diversity as var(Dt)=σ2 Dt=1 S−1 S  s=1 (Ds−E(Dt))2.(2) The covariance structure of kand Dtis var k Dt=Qg gσ2 Dt,(3) where Qis a matrix of dimension N×Nand contains the variance of ki(that is zi(1 – zi)) on the diagonal elements and zero elsewhere. σ2 Dtis a scalar and can be obtained using (2). The vector g(dimension N) contains the covariance between the kiand Dt, and these can be obtained from the Ssamples. The marginal diversity of breed i,mi, is then estimated using the following regression: mi=bDt,ki×ki=cov(Dt,ki) var(ki).(4) Note that the obtained marginal diversities will be positive due to the regression on kiin (4). It is expected that this method will yield accurate estimates for Sbeing large. This stochastic approach was compared to the deterministic method of Simianer et al. [17] outlined in the following. At the end of the time horizon t2Ndifferent combinations of kiwithin kare possible, thus 2Ndifferent vectors might exist, each with probability P(k). For a certain vector kjthe probability can be estimated as follows: P(kj)= N  i=1ki+(−1)kizi.(5) The mean and variance of Dtare E(Dt)= 2N  j=1 P(kj)Dj,and var(Dt)= 2N  j=1 P(kj)D2 j−[E(Dt)]2,(6) Breed contributions to present and future diversity 205 where Djis the diversity according to kj. The marginal diversity of breed iis calculated as the partial derivative of E(Dt) with respect to zi: mi=+ ∂E(Dt) ∂zi ·(7) The positive sign makes it directly comparable with the marginal diversities obtained from (4) (see [17] for computational details). This method will produce correct miestimates (ignoring errors in the diversity measure). However, it becomes obvious that these formulae require the calculation of 2Ntimes the diversity measure, which becomes computationally very difficult or even impossible for large N. 2.2. Core set diversity measures Assume a set of Nbreeds with a known kinship matrix Mof dimension N×N. The maximum variance total (MVT) method forms a core set in which the total genetic variance of a hypothetical quantitative trait is maximised [2]. The relative contributions of the breeds to the MVT core set are estimated as [2] cmvt =1 4M−1F−1NM−1F−4 1NM−11N ·M−11N,(8) where cmvt is the relative contribution vector of dimension Ncontaining the contributions, Fis a vector of dimension Nthat contains the within breed kinship, i.e. F=diag(M), and 1Na vector of dimension Ncontaining ones. The MVT diversity measure (Dmvt) within the core set is then calculated as [2] Dmvt(MVT core set) =1+cmvtF−2cmvtMcmvt.(9) The core set of Eding et al. [7] is built by relative breed contributions in order to maximise the genetic variance in the potential offspring of a conserved population that is obtained by interbreeding the conserved breeds. It will be termed maximum variance offspring (MVO) core set in the following. The relative breed contributions to the MVO core set (stored in the vector cmvo)are estimated as [7] cmvo =M−11N 1NM−11N ·(10) The MVO diversity measure (Dmvo) within the core set can be estimated as [7] Dmvo(MVO core set) =1−cmvoMcmvo.(11) 206 J. Bennewitz et al. Both contribution vectors, cmvt and cmvo, are estimated under the restriction that the contributions are zero or positive and that they sum up to one. If the breeds showed negative contributions, the most negative contribution was set to zero and the contribution vector was recalculated without the corresponding breed. This is repeated until no further negative contribution estimates are observed. In practice the average kinship matrix Mis generally unknown, but can be estimated from molecular marker information [6], resulting in ˆ M.ˆ Mcan then be used in the equations (8)–(11). However, more accurate contribution vectors are obtained if this method is extended with bootstrapping [2]. Briefly, a bootstrap sample bis generated by sampling the individuals within breed and the marker loci across breeds simultaneously with replacement. For each b, the kinship matrix is estimated by a log-linear model [6] and subsequently the corresponding contribution vectors (cmvtband cmvob) are estimated using equations (8) and (10). Additionally the two diversity measures Dmvtband Dmvob are calculated for each busing (9) and (11). A total of Bbootstrap samples are generated. The final bootstrap estimates of the contribution vectors are the following: cmvt =1 B B  b=1 cmvtb,and cmvo =1 B B  b=1 cmvob.(12) The final bootstrap estimates for Dmvtand Dmvoare the following: Dmvt=1 B B  b=1 Dmvtb,and Dmvo=1 B B  b=1 Dmvob.(13) 2.3. Simulation To test the performance of the proposed sampling approach for its ability to estimate accurate expected future diversity and marginal diversities, it was compared by means of simulations with the deterministic approach. Nbreeds (N=10, 20, respectively) were simulated for each replicate, one base breed (consisted of 50 individuals) and N−1 breeds that were formed by fission from the base breed. The number of generations considered was 50. For each individual a number of 20 unlinked genetic marker loci were assumed. For each breed an extinction probability was sampled from the interval 0.1/0.9. Because some constellations were computationally very demanding to simulate and analyse, the number of replicates was restricted to 10. For details of the simulation protocol see [2]. Breed contributions to present and future diversity 207 The pedigree information was recorded during the simulation and was used to calculate the true average kinship matrix M. It was used to calculate the true actual diversities using equations (8)–(11) and to calculate the true expected future diversities and the true marginal diversities by the deterministic formulae (Eqs. (5)–(7)). The genotypes of generation 50 were used to estimate the marker estimated kinship matrix ˆ Mby a weighted log-linear model [6]. The actual diversity was estimated by two different methods. First by the use of ˆ Min equations (8)–(11) and second by the bootstrap approach (Eqs. (12) and (13)). The expected future diversity as well as the marginal diversities of the breeds were estimated using the following three approaches. First by the use of ˆ M in equations (8)–(11) and the deterministic formulae (5)–(7), second by the bootstrap approach (Eqs. (12) and (13), B=100) and the deterministic formulae (5)–(7), and third by the bootstrap approach (Eqs. (12) and (13), B=100) and the sampling algorithm (Eq. (1)–(4)), breeds with ki=0 were removed from all bootstrap samples). For the last approach the number of samples was varied (S=10, 100, 1000, 10000). 2.4. North Eurasian cattle breeds A data set of 44 different native and commercial cattle breeds originating from a large geographic region (i.e. from the Scandinavian and the Baltic countries, Finland, Russia, Byelorussia, Ukraine and Poland) was examined. The Russian breeds included in the study were from the European part of the Russian Federation except the Yakutian cattle, which originate from Asia. The Yakutian cattle make the data set of particular interest because this breed is classified as a Turano-Mongolicus type of cattle [1, 9]. This cattle breed is an endangered native breed in the Sakha Republic (formerly the Yakutia Republic) in the northeast of Siberia in Russia. The data set includes both intensively selected commercial breeds and less selected landraces. Further information of the breeds can be found at http://neurocad.lva.lt/. The breed samples were genotyped at the following 20 microsatellite markers: BM1824, BM2113, ETH10, ETH225, ETH3, HEL5, ILSTS005, INRA023, INRA035, INRA005, BM1818, CSSM66, ETH152, HEL1, HEL13, HEL9, ILSTS006, INRA032, INRA037 and INRA063. A more detailed description of the breed genotype data set will be published elsewhere. It was analysed by the two core set algorithms using the bootstrap approach as described above. A total of 100 (B=100) bootstrap samples were generated and these were stored for the marginal diversity estimation. The relative breed contribution vectors as well as the conserved diversity were estimated using the equations (8)–(13). Genetic 208 J. Bennewitz et al. distances were obtained from the marker estimated kinships as described in [5] and they were visualised in a dendrogram using the PHYLIP software [10]. Expected future diversity as well as marginal diversities were estimated using the sampling algorithm (Eqs. (1)–(4)) applied to the stored 100 bootstrap samples and using the diversity measures obtained from equations (12) and (13). A total of twenty thousand samples were performed (S=20000). The estimation of extinction probabilities needs a substantial amount of data [3,15]. These were not available for the majority of the 44 breeds. Therefore, the breeds were classified into five different risk classes according to their number of breeding females. Simplified extinction probabilities of the breeds were then obtained by assigning extinction probabilities to the corresponding risk class. It was assumed that these are valid for a time horizon tbetween 20 and50yearsintothefuture.Thefivedifferent risk classes and the assigned extinction probabilities zare the following: class one (less than 100 breeding females) z=0.8; class two (between 100 and 1000 breeding females) z=0.6; class three (between 1000 and 5000 breeding females) z=0.4; class four (between 5000 and 10000 breeding females) z=0.2; and class five (more than 10000 breeding females) z=0.02. An extinction probability above zero was assigned to the five, because a completely safe breed is not valid [15]. For the risk class of the breeds in this study as well as for other breed information see the Appendix. For each breed, the conservation potential (CP) was estimated as CPi=zi×mi. The conservation potential quantifies how beneficial it would be in terms of diversity to make a breed completely safe. 3. RESULTS 3.1. Results from the simulations The results from the expected future diversity estimation are presented in Table I. It seems that it is slightly easier to estimate the expected future diversity if the number of breeds included is low. No substantial differences between the results obtained from the different methods were observed. Even the sampling approach with a low number of samples produced reliable future diversity estimates. The correlation between the estimated variances of the expected future diversities were on a similarly high level (not shown) indicating that the second moment can also be estimated accurately by the sampling approach. The average correlation between true and estimated marginal diversities is shown in Table II. The deterministic approach produced more accurate estimates when applied to the bootstrap marker estimated kinship matrices. Breed contributions to present and future diversity 209 Table I. Average correlation between estimated and true expected future diversity for the different methods and number of breeds (N), results from the simulations. MethodaMVT core set MVO core set N=10 N=20 N=10 N=20 11111 2 0.918 0.794 0.923 0.903 3 0.930 –b0.921 –b 4(S=10) 0.888 0.561 0.763 0.938 4(S=100) 0.932 0.911 0.921 0.889 4(S=1000) 0.924 0.887 0.910 0.899 4(S=10000) 0.931 0.901 0.915 0.903 aMethod 1: Use of Mmatrix in equations (8)–(11) and deterministic approach (Eqs. (5)–(7)), true scenario. Method 2: Use of ˆ Mmatrix in equations (8)–(11) and deterministic approach (Eqs. (5)–(7)). Method 3: Use of bootstrap ˆ Mmatrices and deterministic approach (Eqs. (5)–(7)). Method 4: Use of bootstrap ˆ Mmatrices and stochastic approach (Eqs. (1)–(4)), Sdenotes the number of samples. bComputationally too demanding for estimation. Table II. Average correlation between estimated and true marginal diversity for the different methods and number of breeds (N), results from the simulations. MethodaMVT core set MVO core set N=10 N=20 N=10 N=20 11111 2 0.871 0.812 0.856 0.821 3 0.921 –b0.843 –b 4(S=10) 0.489 0.493 0.410 0.236 4(S=100) 0.833 0.688 0.807 0.736 4(S=1000) 0.908 0.853 0.874 0.825 4(S=10000) 0.916 0.858 0.891 0.843 a,bSee Table I. Furthermore, the deterministic approach always produced more accurate estimates than the stochastic approach (Tab. II). Hence, it is advisable to apply the deterministic approach if possible (small/moderate N) and to apply the bootstrap strategy. Otherwise, if the deterministic approach is replaced by the stochastic sampling algorithm, the reduction in accuracy is only small if a reasonably high number of samples are performed. In general, for a given Sit is easier to obtain accurate estimates for a set with small N. Some of the estimates of the marginal diversities were negative due to estimation error. These estimates were set to zero. 216 J. Bennewitz et al. As already mentioned, the estimation of extinction probabilities is a difficult task [3, 12, 15]. Therefore, in this study they were determined by simply assigning probabilities to the defined five risk classes for endangerment. In order to test the sensitivity of the somewhat arbitrary values, two different sets of assigned extinction probabilities were used. The first set was as described above and the extinction probabilities of the second set were exactly the half from those of the first set. Consequently, two marginal diversity estimates for each breed were estimated. A linear model was applied that included the breed and the set of extinction probabilities (either set one or set two) as fixed effects. The null hypothesis was that both marginal diversities within a breed were the same, the alternative hypothesis was that at least for one breed the marginal diversities were not the same. The results of this model suggested to reject the null hypothesis (P<0.01 for both diversity measures). In general, the marginal diversities were somewhat higher for the higher extinction probabilities, however, without changing the ranking order of the breed marginal diversities (not shown). The expected loss of diversity was around 50% lower for the set of lower extinction probabilities. Based on these results, it is beneficial to have more accurate extinction probability estimates because more precise conclusions could be drawn from the results obtained. An alternative to the applied combination of extinction probabilities and diversity measures is the so-called ‘safe set safe set+1’ approach as used in [7]. By using this approach, the ranking of endangered breeds for conservation priority is done according to their contribution to the diversity of a safe (i.e. not endangered) set of breeds. The advantage is that no extinction probabilities are needed, it only has to be decided which breeds form the safe set. 4.3. Conservation of the North Eurasian cattle breed genetic diversity As mentioned above, even without any conservation effort the expected loss of diversity within this set of breeds is low, regardless of the applied diversity measure. If, however, even this small loss is to be reduced, it is not helpful to reduce the extinction probabilities of the most endangered breeds without considering the marginal diversities because there is virtually no relationship between the extinction probability on the one hand and the relative breed contribution and marginal diversity on the other hand as shown in Table IV. Similar results found in a different data set were reported by [17]. Assume a conservation scheme in which the cost to make a breed safe (i.e. bringing its extinction probability close to zero) is independent from its extinction probability and more or less equal for all breeds. Under these conditions, Breed contributions to present and future diversity 217 the breeds with the highest conservation potential would receive the highest priority for the inclusion in the conservation programme. In the present study the five breeds with the highest conservation potential for the MVT diversity measure are Yakutian cattle, Bohus Poll, Ringamala cattle, Red Danish 1970 and Väne cattle. For the MVO, these breeds are the Ringamala cattle, Bohus Poll, Doela cattle, Latvian Blue and Swedish Mountain cattle. If by including them in a conservation plan the extinction probability of these breeds would be close to zero, there would be almost no expected loss of diversity at the end of the time horizon t(not shown). However, these assumptions might only be valid in ex-situ conservation schemes (e.g. transferring a deposit of genetic material from endangered breeds to a genebank), but not in in-situ conservation schemes, where the breeds are conserved within the production system. For the latter situation, Simianer et al. [17] proposed a more sophisticated framework to identify the most efficient conservation plan. The current study provides the prerequisite to apply the methods of [17], given that the unknowns in the method can be replaced by reliable estimates. However, it is reasonable to assume that those three to five breeds with high conservation potential will also be recommended for a conservation plan by the algorithms of [17]. Throughout this study the focus was based exclusively on genetic diversity as a criterion for the conservation of a breed. Other conservation criteria such as adaptation to a specific environment, special traits of economic interest or historical or cultural value are discussed by e.g. [11,13,16]. 5. CONCLUSION It was shown that the sampling algorithm in combination with the two core set genetic diversity measures provides a suitable statistical tool for the marker assisted estimation of present and expected future diversity and of breed marginal diversities, given that extinction probabilities of the breeds are known. The analysis of the North Eurasian cattle breeds revealed that without any conservation efforts the expected loss of diversity during the next 20 to 50 years is between 1 and 3% from actual diversity, provided that the simplified determined extinction probabilities are approximately valid. If this loss was to be reduced or even stopped by a limited conservation fund, it seems to be sufficient to invest the available money in the reduction of the extinction probability of those three to five breeds with the highest marginal diversity and the highest conservation potential. These are not necessarily the most endangered breeds. 218 J. Bennewitz et al. ACKNOWLEDGEMENTS Jörn Bennewitz was supported by a grant from the German Academic Exchange Service (Deutscher Akademischer Austauschdienst, DAAD). REFERENCES [1] Bannikova L.V., Zubareva L.A., Genetic structure of some native and commercial breeds of cattle (Bos taurus) from Eurasia, Russian J. Genet. 31 (1995) 597–607. [2] Bennewitz J., Meuwissen T.H.E., A novel method for the estimation of the relative importance of breeds in order to conserve the total genetic variance, Genet. Sel. Evol. 37 (2005) 315–337. 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[20] Weitzman M.L., On diversity, Quart. J. Econ. 107 (1992) 363–405. 220 J. Bennewitz et al. APPENDIX Information about the breeds included in the field data set. Breed Sample size Sample origin Risk classa Byelorussian Red 23 Byelorussia Five Danish Jersey 41 Denmark Five Estonian Red 40 Estonia Five Finnish Ayrshire 46 Finland Five Finnish Holstein-Friesian 43 Finland Five Icelandic cattle 44 Iceland Five Kholmogory 42 Russia Five Latvian Brown 40 Latvia Five Lithuanian Black and White 41 Lithuania Five Lithuanian Red 40 Lithuania Five Norwegian Dairy cattle 38 Norway Five Polish Black and White 30 Poland Five Swedish Holstein-Friesian 44 Sweden Five Swedish Red and White 39 Sweden Five Yaroslavl 44 Russia Five Istoben 49 Russia Four Suksun 40 Russia Four Blacksided Troender 34 Norway Three Estonian Native 40 Estonia Three Swedish Mountain cattle 41 Sweden Three Telemark cattle 46 Norway Three Ukrainian Whiteheaded 11 Ukraine Three Western Finncattle 41 Finland Three Doela cattle 35 Norway Two Eastern Finncattle 31 Finland Two Eastern Red Polled 11 Norway Two Jutland breed 49 Denmark Two Latvian Blue 40 Latvia Two Latvian Danish Red 40 Latvia Two Lithuanian Light Grey 41 Lithuania Two Lithuanian White Backed 40 Lithuania Two North Finncattle 26 Finland Two Pechora 33 Russia Two Red Danish 1970 39 Denmark Two Swedish Red Polled 34 Sweden Two Ukrainian Grey 30 Ukraine Two Väne cattle 18 Sweden Two Western Fjord cattle 41 Norway Two Western Red Polled 36 Norway Two Yakutian cattle 54 Russia Two Bohus Poll 14 Sweden One Danish Black-Pied 1965 27 Denmark One Fjällnära cattle 15 Sweden One Ringamala cattle 20 Sweden One aClassification done according to the number of breeding females as follows: class one (less than 100 females), class two (between 100 and 1000 females), class three (between 1000 and 5000 females), class four (between 5000 and 10000 females), class five (more than 10000 females). From this, class one is critically endangered and class five not endangered.