Genetic and geographical integration for ruminant production under climate change with particular emphasis on Brazil

Authors

  • Concepta McManus Universidade de Brasília, Instituto de Ciências Biológicas, Asa Norte, CEP: 70910-900, Brasília, DF, Brazil. https://orcid.org/0000-0002-1106-8962
  • Helder Louvandini Centro de Energia Nuclear na Agricultura da Universidade de São Paulo (CENA/USP), Av. Centenário, 303 - São Dimas, CEP: 13416-000, Piracicaba, SP, Brazil.
  • Potira Hermuche LSIE, Departamento de Geografia, ICC Norte, Universidade de Brasília, Asa Norte, CEP: 70910-900, Brasília, DF, Brazil. https://orcid.org/0000-0003-3059-5001
  • Renato Guimarães LSIE, Departamento de Geografia, ICC Norte, Universidade de Brasília, Asa Norte, CEP: 70910-900, Brasília, DF, Brazil. https://orcid.org/0000-0002-9555-043X
  • Osmar Abilio de Carvalho Junior LSIE, Departamento de Geografia, ICC Norte, Universidade de Brasília, Asa Norte, CEP: 70910-900, Brasília, DF, Brazil. https://orcid.org/0000-0002-0346-1684
  • Felipe Pimentel CEUB, 707/907 - Campus Universitário - Asa Norte, CEP: 70790-075, Brasília, DF, Brazil.
  • Daniel Pimentel Universidade de Brasília, Instituto de Ciências Biológicas, Asa Norte, CEP: 70910-900, Brasília, DF, Brazil.
  • Samuel Paiva Embrapa Recursos Genéticos e Biotecnologia, Parque Estação Biológica, PqEB, Av. W5 Norte (final) Caixa Postal 02372, CEP: 70770-917, Brasília, DF, Brazil.
  • Vanessa Peripolli Instituto Federal Catarinense, Campus Araquari, Rodovia BR 280, km 27, CEP: 89245-000, Araquari, SC, Brazil. https://orcid.org/0000-0002-0463-4727

DOI:

https://doi.org/10.31893/avr.2022009

Keywords:

georeferencing, landscape genetics, molecular markers, stressful environments

Abstract

The use of georeferencing technologies and genetic information has increased to integrate management and planning of livestock production systems, predict adaptive capacities, and aid in developing strategies for national Animal Genetic Resource Conservation Programs. Researchers and farmers can use this information to define conservation objectives for individual breeds and examine environmental factors that affect extinction risk, such as disease threats. Molecular markers and geographic information come together in landscape genetics, a combination of landscape ecology and population genetics, to provide information on the interaction between landscape and evolutionary processes. Results reveal attributes that affect genetic adaptation to specific environmental stressors such as diseases, parasites, extreme heat, vegetation type, lack of water, or combinations. Recent preliminary studies in Brazil used these tools to identify the regional usage patterns for animal production based on environmental criteria and breed distribution data. The results have been used as a further criterion to optimise in situ, and ex situ conservation schemes and plan expansion and adaptation of production systems. The use of production environment descriptors and climatic and genetic information will help maintain animal production systems in a changing world.

References

Al-Araimi NA, Al-Atiyat RM, Gaafar OM, Vasconcelos R, Luzuriaga-Neira A, Eisa MO, Amir N, Benaissa MH, Alfaris AA, Aljumaah RS, Elnakhla SM, Salem MMI, Ishag IA, Khasmi ME, Beja-Pereira A (2017) Maternal genetic diversity and phylogeography of native Arabian goats. Livestock Science 206:88-94. https://doi.org/10.1016/j.livsci.2017.09.017.

Amane A, Belay G, Nasser Y, Kyalo M, Dessie T, Kebede A. Getachew T, Domelevo Entfellner J-B, Edea Z, Hanotte O, Mekuriaw G (2020) Genome wide insights of Ethiopian indigenous sheep populations reveal the population structure related to tail morphology and phylogeography. Genes and Genomics. 42:1169-1178. doi: 10.1007/s13258-020-00984-y.

Angers B, Magnan P, Plante M, Bernatchez L (1999) Canonical correspondence analysis for estimating spatial and environmental effects on microsatellite gene diversity in brook charr (Salvelinus fontinalis). Molecular Ecology 8:1043–1053.

Avise JC (1998) The history and purview of phylogeography: a personal reflection. Molecular Ecology. 7:371–379

Avise JC, Arnold J, Ball RM, Bermingham E, Lamb T, Neigel JE, Reeb CA, Saunders NC (1987) Intraspecific Phylogeography: The Mitochondrial DNA Bridge Between Population Genetics and Systematics. Annual Review of Ecology and Systematics 18: 489–522.

Bajardi P, Barrat A, Savini L, Colizza V (2012) Optimising surveillance for livestock disease spreading through animal movements. Journal of the Royal Society Interface 9:2814-2825.

Berners-Lee M, Kennelly C, Watson R, Hewitt CN, Kapuscinski AR, Locke KA, Peters CJ (2018) Current global food production is sufficient to meet human nutritional needs in 2050 provided there is radical societal adaptation. Elementa: Science of the Anthropocene 6:52.

Bricarello PA, Gennari SM, Oliveira-Sequeira TCG, Vaz CMSL, Goncalves de Goncalves I, Echevarria FAM (2004) Worm burden and immunological responses in Corriedale and Crioula Lanada sheep following natural infection with Haemonchus contortus. Small Ruminant Research 51:75-83.

Campos E, Cuéllar J, Slavador O, García-Trejo EA, Pereira F (2020) The genetic diversity and phylogeography of Mexican domestic sheep. Small Ruminant Research 187:106109

Carvalho LFR, Melo CB, McManus C, Haddad JP (2012) Use of satellite images for geographical localisation of livestock holdings in Brazil. Preventative Veterinary Medicine 103:74–77. doi: 10.1016/j.prevetmed.2011.08.006. pmid:21917345

Castanheira M, Paiva SR, Louvandini H, Landim A, Fiorvanti MCS, Dallago BS, Correa PS, McManus C (2010) Use of heat tolerance traits in discriminating between groups of sheep in central Brazil. Tropical Animal Health and Production42:1821–1828. doi:10.1007/s11250-010-9643-x

Cavalli-Sforza LL, Menozzi P, Piazza A. (1994) The History and Geography of Human Genes (Princeton University Press, Princeton, New Jersey, USA.

Cesconeto RJ, McManus CM, Paiva SR, Cobuci JA, Joost S, Braccini Neto J (2017) Landscape Genomic Approach to Detect Selection Signatures in Locally Adapted Brazilian Swine Genetic Groups. Ecology and Evolution, 7:9544-9556.

Ciani E, Ciampolini R, D'Andrea M, Castellana E, Cecchi F, Incoronato C, d'Angelo F, Albenzio M, Pilla F, Matassino D, Cianci D (2013) Analysis of genetic variability within and among Italian sheep breeds reveals population stratification and suggests the presence of a phylogeographic gradient, Small Ruminant Research 112:21-27, https://doi.org/10.1016/j.smallrumres.2012.12.013.

Colli L, Lancioni H, Cardinali I (2015) Whole mitochondrial genomes unveil the impact of domestication on goat matrilineal variability. BMC Genomics 16:1115. https://doi.org/10.1186/s12864-015-2342-2

Corander J, Siren J, Arjas E (2008) Bayesian spatial modeling of genetic population structure. Computational Statistics 23:111–129.

Costa NS, Hermuche P, Cobuci JA, Paiva SR, Guimaraes RF, Carvalho Jr. AO, Gomes RAT, Costa CN, McManus CM (2014) Georeferenced evaluation of genetic breeding value patterns in Brazilian Holstein cattle. Genetics and Molecular Research 13: 9806-9816

Costa NS, da Silva MV, Panetto JC, Machado MA, Seixas L, Peripolli V, Guimarães RF, Carvalho OA, Vieira RA, McManus C (2020) Spatial dynamics of the Girolando breed in Brazil: analysis of genetic integration and environmental factors. Tropical Animal Health and Production 52:3869-83.

Crida A, Manel S (2007) WOMBSOFT: an R package that implements the Wombling method to identify genetic boundary. Molecular Ecology Notes 7:588-91.

Daltro DS, Fischer V, Alfonzo EPM, Dalcin VC, Stumpf MT, Kolling GJ, Silva MVGB, McManus C (2017) Infrared thermography as a method for evaluating the heat tolerance in dairy cows. Revista Brasileira de Zootecnia 46:374-383. https://doi.org/10.1590/s1806-92902017000500002

de Sá MEP, de Sá CVG, Nicolino RR, Haddad JPA, McManus C, Seixas L, de Melo CB (2018) Data on network of live cattle exports from Brazil. Data in brief 19:1963-1969.

Diniz-Filho JF, Soares TN, Lima JS, Dobrovolski R, Landeiro VL, Telles MPC, Rangel TF, Bini LM (2013) Mantel test in population genetics. Genetics and Molecular Biology 36:475–485. doi: 10.1590/S1415-47572013000400002

Durand E, Jay F, Gaggiotti OE (2009) Spatial inference of admixture proportions and secondary contact zones. Molecular Biology and Evolution 26:1963–1973.

Egito AA, Albuquerque SM, Mariante AS (1999) Situação Atual da Caracterização Genética Animal na Embrapa Recursos Genéticos e Biotecnologia In: Simpósio de Recursos Genéticos para América Latina e Caribe – SIRGEALC, 2. Anais... Brasília: Embrapa Recursos Genéticos e Biotecnologia, 1999. CD-ROM.

Escarcha JF, Lassa JA, Zander KK (2018) Livestock Under Climate Change: A Systematic Review of Impacts and Adaptation. Climate 6:54.

Euclides Filho K (2004) Supply chain approach to sustainable beef production from a Brazilian perspective. Livestock Production Science 90:53-61.

Eydivandi S, Roudbar MA, Ardestani SS, Momen M, Sahana G (2021) A selection signatures study among Middle Eastern and European sheep breeds. Journal of Animal Breeding and Genetics 138:574-588

Fung T, Ledrew E (1987) Application of principal components analysis to change detection. Photogrammetric Engineering & Remote Sensing 53:1649–1658.

Galal S, Boyazoglu J (2001) Preparation of the First Report on the State of the World's Animal Genetic Resources: Guidelines for the Development of Country Reports: Animal genetic resources information. 30 http://www.fao.org/docrep/004/y1100m/y1100m03.htm. Accessed on: February 21, 2021

Gaughan J, Cawdell-Smith AJ (2015) Impact of Climate Change on Livestock Production and Reproduction. In: Sejian V, Gaughan J, Baumgard L, Prasad C. Eds., Climate Change Impact on Livestock: Adaptation and Mitigation, Springer: New Delhi, India, pp. 51–60.

Godber OF, Wal R (2014) Livestock and food security: Vulnerability to population growth and climate change. Global Change Biology 20:3092–3102.

Guillot G, Estoup A, Mortier F, Cosson JF (2005) A spatial statistical model for landscape genetics. Genetics 170:1261–1280.

Guillot G, Leblois R, Coulon A, Frantz AC (2009) Statistical methods in spatial genetics. Molecular Ecology 18:4734–4756.

Hamblin MT, Casa AM, Sun H, Murray SC, Paterson AH, Aquadro CF, Kresovich S (2006) Challenges of detecting directional selection after a bottleneck: lessons from sorghum bicolor. Genetics 173:953–964. Doi: 10.1534/genetics.105.054312

Hermuche P, Guimarães RF, Carvalho Júnior OA, Paiva SR, Gomes RAT, McManus CM (2013a) Environmental factors that affect sheep production in Brazil. Applied Geography 44:172–181. doi: 10.1016/j.apgeog.2013.07.016

Hermuche PM, Guimarães RF, Carvalho Junior OA, Paiva SR, Gomes RAT, McManus C: (2013b) Priority areas for expansion of sheep production in Brazil using landscape controls. Journal of Applied Geography 44: 172–181.

Hermuche PM, Maranhão RLA, Guimaraes RF, Carvalho Junior OA, Paiva SR, Gomes RAT, McManus C (2013c) Dynamics of sheep production in Brazil. International Journal of Geo-Information 2: 665–679.

Hermuche PM, Silva NC, Guimarães RF, Carvalho Júnior OA, Paiva SR, Gomes RAT, McManus CM (2012) Dynamics of sheep production in Brazil using principal components and maps of auto-organization characteristics. Revista Brasileira de Cartografia 64:821–832.

Herpin P, Charley B (2008) What future for research in animal production and animal health? INRA Productions Animimales 21:137-144.

Herrero M, Thornton PK, Power B, Bogard JR, Remans R, Fritz S, Gerber JS, Nelson G, See L, Waha K, Watson RA (2017) Farming and the geography of nutrient production for human use: a transdisciplinary analysis. The Lancet Planetary Health 1:e33-42.

Joost S, Colli L, Baret PV, Garcia JF, Boettcher PJ, Tixier‐Boichard M, Ajmone‐Marsan P (2010) Globaldiv Consortium. Integrating georeferenced multiscale and multidisciplinary data for the management of biodiversity in livestock genetic resources. Animal genetics.41:47-63.

Joshi MB, Rout PK, Mandal AK, Tyler-Smith C, Singh L, Thangaraj K (2004) Phylogeography and Origin of Indian Domestic Goats. Molecular Biology and Evolution 21:454–462 https://doi.org/10.1093/molbev/msh038

Karesh WB, Dobson A, Lloyd-Smith JO, Lubroth J, Dixon MA, Bennett M, Aldrich S, Harrington T, Formenty P, Loh EH, Machalaba CC (2012) Ecology of zoonoses: natural and unnatural histories. The Lancet 1380:1936-45.

Kc KB, Dias GM, Veeramani A, Swanton CJ, Fraser D, Steinke D, Lee E, Wittman H, Farber JM, Dunfield K, McCann K (2018) When too much isn't enough: Does current food production meet global nutritional needs? PLoS ONE 13:e0205683. https://doi.org/10.1371/journal.pone.0205683

Kijas JW, Townley D, Dalrymple BP, Heaton MP, Maddox JF, McGrath A, Wilson P, Ingersoll RG, McCulloch R, McWilliam S, Tang D (2009) A Genome Wide Survey of SNP Variation Reveals the Genetic Structure of Sheep Breeds. PLoS ONE 4:e4668. https://doi.org/10.1371/journal.pone.0004668

Kohonen T (1988) Self-Organization and Associative Memory. Berlin: Springer-Verlag, 312p.

Latter BDH (1973) The island model of population differentiation: a general solution. Genetics 73:147-157.

Leempoel K, Duruz S, Rochat E, Widmer I, Orozco-terWengel P and Joost S (2017) Simple Rules for an Efficient Use of Geographic Information Systems in Molecular Ecology. Frontiers in Ecology and Evololution 5:33. doi: 10.3389/fevo.2017.00033

Liu J, Lu Z, Yuan C, Wang F, Yang B (2020). Phylogeography and Phylogenetic Evolution in Tibetan Sheep Based on MT-CYB Sequences. Animals 10: 1177.

Manel S, Schwartz MK, Luikart G and Taberlet P (2003). Landscape genetics: combining landscape ecology and population genetics. Trends in Ecology and Evololution 18:189-197

Maranhão RLA, Carvalho Jr AO, Hermuche P, Gomes RAT, McManus C, Guimaraes RF (2019) The Spatiotemporal Dynamics of Soybean and Cattle Production in Brazil. Sustainability 11:2150

Mariante AdaS, Cavalcante N (2006) Animais do Descobrimento: raças domésticas da história do Brasil. Brasília, Embrapa Sede/ Embrapa Recursos Genéticos e Biotecnologia, 232p.

Marques PR, Barcellos JOJ, McManus C, Oaigen RP, Collares FC, Canozzi MEA, Lampert VN (2011) Competitiveness of beef farming in Rio Grande do Sul State, Brazil. Agricultural Systems 104:689-693.

Marske KA (2016). Phylogeography. In: Kilman RM (ed) Encyclopedia of Evolutionary Biology pp. 291-296

Martien KK, Gregovich DP (2008) Comparative performance testing of spatially explicit genetic analytical methods. Paper SC/60/SD2 submitted to the IWC Scientific Committee, https://swfsc.noaa.gov/publications/CR/2008/2008Martien2.pdf,p 1-10, Accessed on: April 27, 2021.

McManus C, Barcellos JOJ, Formenton BK, Hermuche PM, Carvalho OAd Jr, Guimarães R (2016a) Dynamics of Cattle Production in Brazil. PLoS ONE 11:e0147138. doi: 10.1371/journal.pone.0147138

McManus C, Dallago BS, Lehugeur C, Ribeiro LA, Hermuche P, Guimarães RF, de Carvalho Júnior OA, Paiva SR (2016b) Patterns of heat tolerance in different sheep breeds in Brazil. Small Ruminant Research 144:290-9.

McManus C, Hermuche PM, Paiva SR, Melo CB, Mendes CQ (2014a) Geographical distribution of sheep breeds in Brazil and their relationship with climatic and environmental factors as risk classification for conservation. Brazilian Journal of Science and Technology 1:3. doi: 10.1186/2196-288x-1-3

McManus CM, Hermuche P, Guimarães RF, de Carvalho Júnior OA, Dallago BSL, Vieira RA, de Faria DA, Blackburn H, Moraes JCF, Souza CH, Facó O (2021a) Integration of georeferenced and genetic data for the management of biodiversity in sheep genetic resources in Brazil. Tropical Animal Health and Production 53:1-15.

McManus C, Hermuche PM, Paiva SR, Guimarães RF, Carvalho Junior, OA, Blackburn HD (2021b) Gene bank collection strategies based upon geographic and environmental indicators for beef breeds in the United States of America. Livestock Science 254:104766.

McManus C, Paiva SR, Araújo, RO (2010) Genetics and breeding of sheep in Brazil. Revista Brasileira de Zootecnia 39:236-246.

McManus CM, Faria DA, Bem A de, Maranhão AQ, Paiva SR (2020a) Physiology and genetics of heat stress in cattle. CAB Reviews 15:1-12.

McManus CM, Faria DA, Lucci CM, Louvandini H, Pereira SA, Paiva SR, (2020b) Heat stress effects on sheep: Are hair sheep more heat resistant?, Theriogenology, 155:157-167.

McManus C, Hermuche P, Paiva SR, Daltro D, Afonso EM, Facó O (2014b) Distribution of Goat Breeds in Brazil and Their Relationship with Environmental Controls. Bioscience Journal 30:1819-1836

McManus C, Hermuche P, Paiva SR, Ferrugem-Moraes JC, de Melo CB, Mendes CQ (2014c) Geographical distribution of sheep breeds in Brazil and their relationship with climatic and environmental factors as risk classification for conservation. Brazilian Journal of Science and Technology 1:3.

McManus C, Hermuche PM, Guimaraes RF, Carvalho Jr O, Silva NS, Carvalho LFR, Dallago BS, Moraes JF, de Sousa CH, Faco O, Araujo AM, Azevedo HC, Carneiro PS, Santos SA, Mattos PS, Lobo RNB, Paiva SR (2021c) Integration of Geo-referenced and genetic data for management of bio-diversity in sheep genetic resources in Brazil. Tropical Animal Health and Production 53:126. doi: 10.1007/s11250-021-02573-x.

McManus C, Tanure C, Peripolli V, Seixas L, Fischer V, Gabbi A, Menegassi S, Stumpf M, Kolling G, Dias EA, Costa Jr JB (2016c) Infrared thermography in animal production: An overview. Computers and Electronics in Agriculture 123:10-16.

McManus C, Paiva SR, Caetano AR, Hermuche P, Guimarães RF, Carvalho Junior OA, Braga R, Carneiro PLS, Ferrugem-Moraes J, Souza, CJH, Faco O, Santos SA, Azevedo HC, Araujo AM, Façanha DAE, Ianella P (2020c) Landscape genetics of sheep in Brazil using SNP markers. Small Ruminant Research 192:106239.

McManus C, Paludo GR, Louvandini H, Gugel R, Sasaki LSB, Paiva SR (2009b) Heat tolerance in Brazilian sheep: physiological and blood parameters. Tropical Animal Health and Production 41:95-101.

McManus, C, Prescott E, Paludo GR, Bianchini E, Louvandini H, Mariante, AS (2009a) Heat tolerance in naturalised Brazilian cattle breeds. Livestock Science 120:256–264.

Meadows JR, Li K, Kantanen J, Tapio M, Sipos W, Pardeshi V, Gupta V, Calvo JH, Whan V, Norris B, Kijas JW (2005) Mitochondrial sequence reveals high levels of gene flow between breeds of domestic sheep from Asia and Europe. Journal of Heredity 96:494-501.

Monmonier MS (1973) Maximum-difference barriers: an alternative numerical regionalisation method. Geographical Analysis 3:245-61

Nelson-Flower MJ, Hockey PA, O'Ryan C, Ridley AR (2012) Inbreeding avoidance mechanisms: dispersal dynamics in cooperatively breeding southern pied babblers. Journal of Animal Ecology 81:876-83. doi: 10.1111/j.1365-2656.2012.01983.x.

O'Neill CJ, Swain DL, Kadarmideen HN (2010). Evolutionary process of Bos taurus cattle in favourable versus unfavourable environments and its implications for genetic selection. Evolutionary Applications 3:422-433.

Paim TP, Ianella P, Paiva SR, Caetano AR, McManus CM (2018) Detection and evaluation of selection signatures in sheep Pesquisa agropecuaria brasileira 53:527-539.

Pariset L, Joost S, Gargani M, Valentini A (2012) Landscape Genomics in Livestock. In: Çaliskan M (ed) Analysis of Genetic Variation in Animals. INTECH, Rijeka, Croatia.

Prasad CS, Sejian V (2015) Climate change impact on livestock sector: Visioning 2025. In: Sejain V, Gaughan J, Baumgard L, Prasad C (eds) Climate Change Impact on Livestock: Adaptation and Mitigation, Springer: Berlin, Germany, pp: 479–489.

Pritchard J, Stephens M, Donnelly P (2000b) Inference of population structure using multi-locus genotype data. Genetics 155:945–959.

Resende RT, Piepho HP, Rosa GJ, Silva-Junior OB, Silva FF, de Resende MDV, Grattapaglia D (2021) Enviromics in breeding: applications and perspectives on envirotypic-assisted selection. Theoretical and Applied Genetics 134:95-112.

Rivera‐Ferre MG, López‐i‐Gelats F, Howden M, Smith P, Morton JF, Herrero M (2016) Re‐framing the climate change debate in the livestock sector: mitigation and adaptation options. Wiley Interdisciplinary Reviews: Climate Change 7:869-92.

Robinson TP, Wint GRW, Conchedda G, Van Boeckel TP, Ercoli V, Palamara E (2014) Mapping the Global Distribution of Livestock. PLoS ONE 9: e96084.

Scherf BD (2008) World Watch List for Animal Diversity. Food and Agriculture Organization of the United Nations, Rome.

Scholtz MM, Furstenburg D, Maiwashe A, Makgahlela ML, Theron HE. van der Westhuizen J (2010) Environmental-genotype responses in livestock to global warming: A Southern African perspective. South African Journal of Animal Science 40:408-413.

Seo SN, Mendelsohn R (2008) Measuring impacts and adaptations to climate change: a structural Ricardian model of African livestock management. Agricultural Economics 38:151-165

Seo SN, McCarl BA, Mendelsohn R (2010) From beef cattle to sheep under global warming? An analysis of adaptation by livestock species choice in South America. Ecological Economics 69:2486-2494.

Serrano G, Egito AA, McManus C, Mariante AS (2014) Genetic diversity and population structure of Brazilian native bovine breeds. Pesquisa Agropecuária Brasileira 39:543-549.

Silva BD, Castro EA, Souza CJ, Paiva SR, Sartori R, Franco MM, Azevedo HC, Silva TA, Vieira AM, Neves JP, Melo EO (2011) A new polymorphism in the Growth and Differentiation Factor 9 (GDF9) gene is associated with increased ovulation rate and prolificacy in homozygous sheep. Animal Genetics 42:89–92. doi: 10.1111/j.1365-2052.2010.02078.x

Silva RG (2000) Introdução a bioclimatologia animal. São Paulo: FAPESP/Nobel.

Silva MC (2015) Genômica de Populações e Genética Geográfica de Bovinos Pantaneiros e Curraleiro Pé –Duro com uso de Polimorfismos de Base Única (SNP), Doutorado, Universidade Federal de Goias.

Slatkin M, Voelm L (1991) FST in a hierarchical island model. Genetics 127:627-629.

Sork VL, Aitken SN, Dyer RJ, Eckert AJ, Legendre P, Neale DB (2013) Putting the landscape into the genomics of trees: approaches for understanding local adaptation and population responses to changing climate. Tree Genetics & Genomes 9:901-911

Souza ACBD, Egito AA, Peripolli V, McManus CM (2022). Bovine landscape genetics in Brazil. Scientia Agricola 79. DOI: 10.1590/1678-992X-2020-0142

Storfer A, Murphy MA, Evans JS, Goldberg CS, Robinson S, Spear SF, Dezzani R, Delmelle E, Vierling L, Waits LP (2007) Putting the 'landscape' in landscape genetics. Heredity 98:128–142

Stott GH (1981) What is animal stress and how is it measured? Journal of Animal Science 52:150-153.

Stucki S, Orozco‐terWengel P, Forester BR, Duruz S, Colli L, Masembe C, Negrini R, Landguth E, Jones MR, Nextgen Consortium, Bruford MW (2017) High performance computation of landscape genomic models including local indicators of spatial association. Molecular Ecology Resources 17:1072– 1089.

Wolfe DW, Ziska L, Petzoldt C, Seaman A, Chase L, Hayhoe K (2008) Projected change in climate thresholds in the Northeastern US: implications for crops, pests, livestock, and farmers. Mitigation and Adaptation Strategies for Global Change 13:555–575

Wright S (1943) Isolation by distance. Genetics 28: 114–138.

Yahdjian L, Sala OE (2008) Climate Change Impacts on South American Rangelands. Rangelands 30:34-39

Zhang YW, McCarl BA, Jones JPH (2017) An Overview of Mitigation and Adaptation Needs and Strategies for the Livestock Sector. Climate 5:95.

3D Surface of spatialised genetic distances with a) ascending peaks representing genetic discontinuities for hair sheep in northeastern Brazil.

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07-05-2022

How to Cite

McManus, C., Louvandini, H., Hermuche, P., Guimarães, R., de Carvalho Junior, O. A., Pimentel, F., Pimentel, D., Paiva, S., & Peripolli, V. . (2022). Genetic and geographical integration for ruminant production under climate change with particular emphasis on Brazil. Applied Veterinary Research, 1(2), e2022009. https://doi.org/10.31893/avr.2022009

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Review Article