Enteric methane mitigation strategies for ruminant livestock systems in the Latin America and Caribbean region: A meta-analysis

Latin America and Caribbean (LAC) is a developing region characterized for its importance for global food security, producing 23 and 11% of the global beef and milk production, respectively. The region's ruminant livestock sector however, is under scrutiny on environmental grounds due to its large contribution to enteric methane (CH4) emissions and influence on global climate change. Thus, the identification of effective CH4 mitigation strategies which do not compromise animal performance is urgently needed, especially in context of the Sustainable Development Goals (SDG) defined in the Paris Agreement of the United Nations. Therefore, the objectives of the current study were to: 1) collate a database of individual sheep, beef and dairy cattle records from enteric CH4 emission studies conducted in the LAC region, and 2) perform a meta-analysis to identify feasible enteric CH4 mitigation strategies, which do not compromise animal performance. After outlier's removal, 2745 animal records (65% of the original data) from 103 studies were retained (from 2011 to 2021) in the LAC database. Potential mitigation strategies were classified into three main categories (i.e., animal breeding, dietary, and rumen manipulation) and up to three subcategories, totaling 34 evaluated strategies. A random effects model weighted by inverse variance was used (Comprehensive Meta-Analysis V3.3.070). Six strategies decreased at least one enteric CH4 metric and simultaneously increased milk yield (MY; dairy cattle) or average daily gain (ADG; beef cattle and sheep). The breed composition F1 Holstein × Gyr decreased CH4 emission per MY (CH4IMilk) while increasing MY by 99%. Adequate strategies of grazing management under continuous and rotational stocking decreased CH4 emission per ADG (CH4IGain) by 22 and 35%, while increasing ADG by 22 and 71%, respectively. Increased dietary protein concentration, and increased concentrate level through cottonseed meal inclusion, decreased CH4IMilk and CH4IGain by 10 and 20% and increased MY and ADG by 12 and 31%, respectively. Lastly, increased feeding level decreased CH4IGain by 37%, while increasing ADG by 171%. The identified effective mitigation strategies can be adopted by livestock producers according to their specific needs and aid LAC countries in achieving SDG as defined in the Paris Agreement.Fil: Congio, Guilhermo Francklin de Souza. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; Brasil. Corporación Colombiana de Investigación Agropecuaria; ColombiaFil: Bannink, André. University of Agriculture Wageningen; Países BajosFil: Mayorga Mogollón, Olga Lucía. Corporación Colombiana de Investigación Agropecuaria; ColombiaFil: Jaurena, Gustavo. Universidad de Buenos Aires. Facultad de Agronomia. Departamento de Producción Animal. Cátedra de Nutrición Animal; ArgentinaFil: Gonda, Horacio Leandro. Uppsala Universitet; SueciaFil: Gere, José Ignacio. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Patobiología Veterinaria - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Patobiología Veterinaria; ArgentinaFil: Cerón Cucchi, María Esperanza. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Patobiología; ArgentinaFil: Ortiz Chura, Abimael. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación en Ciencias Veterinarias y Agronómicas. Instituto de Patobiología; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tieri, María Paz. Universidad Tecnológica Nacional. Facultad Regional Rafaela; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Investigación de la Cadena Láctea. - Instituto Nacional de Tecnología Agropecuaria. Centro Regional Santa Fe. Estación Experimental Agropecuaria Rafaela. Instituto de Investigación de la Cadena Láctea; ArgentinaFil: Hernandez, Olegario. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Tucuman-Santiago del Estero; ArgentinaFil: Ricci, Patricia. Instituto Nacional de Tecnologia Agropecuaria. Centro Regional Buenos Aires Sur. Estacion Experimental Agropecuaria Balcarce. Instituto de Innovación Para la Producción Agropecuaria y El Desarrollo Sostenible. Grupo Vinculado Estacion Experimental Agropecuaria Cuenca del Salado Al Ipads | Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Mar del Plata. Instituto de Innovación Para la Producción Agropecuaria y El Desarrollo Sostenible. Grupo Vinculado Estacion Experimental Agropecuaria Cuenca del Salado Al Ipads.; ArgentinaFil: Juliarena, María Paula. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Lombardi, Banira. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Abdalla, Adibe Luiz. Universidade de Sao Paulo; BrasilFil: Abdalla Filho, Adibe Luiz. Universidade de Sao Paulo; BrasilFil: Berndt, Alexandre. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Oliveira, Patrícia Perondi Anchão. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Henrique, Fábio Luis. Colegios Asociados de Uberaba; BrasilFil: Monteiro, Alda Lúcia Gomes. Universidade Federal do Paraná; BrasilFil: Borges, Luiza Ilha. Universidade Federal do Paraná; BrasilFil: Ribeiro Filho, Henrique Mendonça Nunes. Universidade Federal de Santa Catarina; BrasilFil: Ribeiro Pereira, Luiz Gustavo. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Tomich, Thierry Ribeiro. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Campos, Mariana Magalhães. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Machado, Fernanda Samarini. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Marcondes, Marcos Inácio. Universidade Federal de Viçosa.; BrasilFil: Mercadante, Maria Eugênia Zerlotti. Agencia de Tecnología Agroindustrial de Sao Paulo; ArgentinaFil: Sakamoto, Leandro Sannomiya. Agencia de Tecnología Agroindustrial de Sao Paulo; ArgentinaFil: Albuquerque, Lucia Galvão. Universidade Estadual Paulista Julio de Mesquita Filho; BrasilFil: Carvalho, Paulo César de Faccio. Universidade Federal do Rio Grande do Sul; BrasilFil: Hristov, Alexander Nikolov. State University of Pennsylvania; Estados Unidos. University of Agriculture Wageningen; Países Bajos. Universidade de Sao Paulo; Brasil. Corporación Colombiana de Investigación Agropecuaria; Colombi

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution

Geographic patterns of tree dispersal modes in Amazonia and their ecological correlates

Aim: To investigate the geographic patterns and ecological correlates in the geographic distribution of the most common tree dispersal modes in Amazonia (endozoochory, synzoochory, anemochory and hydrochory). We examined if the proportional abundance of these dispersal modes could be explained by the availability of dispersal agents (disperser-availability hypothesis) and/or the availability of resources for constructing zoochorous fruits (resource-availability hypothesis). Time period: Tree-inventory plots established between 1934 and 2019. Major taxa studied: Trees with a diameter at breast height (DBH) ≥ 9.55 cm. Location: Amazonia, here defined as the lowland rain forests of the Amazon River basin and the Guiana Shield. Methods: We assigned dispersal modes to a total of 5433 species and morphospecies within 1877 tree-inventory plots across terra-firme, seasonally flooded, and permanently flooded forests. We investigated geographic patterns in the proportional abundance of dispersal modes. We performed an abundance-weighted mean pairwise distance (MPD) test and fit generalized linear models (GLMs) to explain the geographic distribution of dispersal modes. Results: Anemochory was significantly, positively associated with mean annual wind speed, and hydrochory was significantly higher in flooded forests. Dispersal modes did not consistently show significant associations with the availability of resources for constructing zoochorous fruits. A lower dissimilarity in dispersal modes, resulting from a higher dominance of endozoochory, occurred in terra-firme forests (excluding podzols) compared to flooded forests. Main conclusions: The disperser-availability hypothesis was well supported for abiotic dispersal modes (anemochory and hydrochory). The availability of resources for constructing zoochorous fruits seems an unlikely explanation for the distribution of dispersal modes in Amazonia. The association between frugivores and the proportional abundance of zoochory requires further research, as tree recruitment not only depends on dispersal vectors but also on conditions that favour or limit seedling recruitment across forest types

Geography and ecology shape the phylogenetic composition of Amazonian tree communities

Aim: Amazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types. Location: Amazonia. Taxon: Angiosperms (Magnoliids; Monocots; Eudicots). Methods: Data for the abundance of 5082 tree species in 1989 plots were combined with a mega-phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran\u27s eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny. Results: In the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white-sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R$^{2}$ = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R$^{2}$ = 28%). A greater number of lineages were significant indicators of geographic regions than forest types. Main Conclusion: Numerous tree lineages, including some ancient ones (>66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long-standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Author correction: One sixth of Amazonian tree diversity is dependent on river floodplains

In the version of the article initially published, the affiliation of Edgardo Manuel Latrubesse was incorrect and has now been amended to Environmental Sciences Graduate Program-CIAMB, Federal University of Goiás, Goiânia, Brazil in the HTML and PDF versions of the article

Mapping density, diversity and species-richness of the Amazon tree flora

Using 2.046 botanically-inventoried tree plots across the largest tropical forest on Earth, we mapped tree species-diversity and tree species-richness at 0.1-degree resolution, and investigated drivers for diversity and richness. Using only location, stratified by forest type, as predictor, our spatial model, to the best of our knowledge, provides the most accurate map of tree diversity in Amazonia to date, explaining approximately 70% of the tree diversity and species-richness. Large soil-forest combinations determine a significant percentage of the variation in tree species-richness and tree alpha-diversity in Amazonian forest-plots. We suggest that the size and fragmentation of these systems drive their large-scale diversity patterns and hence local diversity. A model not using location but cumulative water deficit, tree density, and temperature seasonality explains 47% of the tree species-richness in the terra-firme forest in Amazonia. Over large areas across Amazonia, residuals of this relationship are small and poorly spatially structured, suggesting that much of the residual variation may be local. The Guyana Shield area has consistently negative residuals, showing that this area has lower tree species-richness than expected by our models. We provide extensive plot meta-data, including tree density, tree alpha-diversity and tree species-richness results and gridded maps at 0.1-degree resolution