Global priorities for conservation across multiple dimensions of mammalian diversity

Conservation priorities that are based on species distribution, endemism, and vulnerability may underrepresent biologically unique species as well as their functional roles and evolutionary histories. To ensure that priorities are biologically comprehensive, multiple dimensions of diversity must be considered. Further, understanding how the different dimensions relate to one another spatially is important for conservation prioritization, but the relationship remains poorly understood. Here, we use spatial conservation planning to (i) identify and compare priority regions for global mammal conservation across three key dimensions of biodiversity-taxonomic, phylogenetic, and traits-and (ii) determine the overlap of these regions with the locations of threatened species and existing protected areas. We show that priority areas for mammal conservation exhibit low overlap across the three dimensions, highlighting the need for an integrative approach for biodiversity conservation. Additionally, currently protected areas poorly represent the three dimensions of mammalian biodiversity. We identify areas of high conservation priority among and across the dimensions that should receive special attention for expanding the global protected area network. These high-priority areas, combined with areas of high priority for other taxonomic groups and with social, economic, and political considerations, provide a biological foundation for future conservation planning efforts

Machine Learning Approach for Prescriptive Plant Breeding

We explored the capability of fusing high dimensional phenotypic trait (phenomic) data with a machine learning (ML) approach to provide plant breeders the tools to do both in-season seed yield (SY) prediction and prescriptive cultivar development for targeted agro-management practices (e.g., row spacing and seeding density). We phenotyped 32 SoyNAM parent genotypes in two independent studies each with contrasting agro-management treatments (two row spacing, three seeding densities). Phenotypic trait data (canopy temperature, chlorophyll content, hyperspectral reflectance, leaf area index, and light interception) were generated using an array of sensors at three growth stages during the growing season and seed yield (SY) determined by machine harvest. Random forest (RF) was used to train models for SY prediction using phenotypic traits (predictor variables) to identify the optimal temporal combination of variables to maximize accuracy and resource allocation. RF models were trained using data from both experiments and individually for each agro-management treatment. We report the most important traits agnostic of agro-management practices. Several predictor variables showed conditional importance dependent on the agro-management system. We assembled predictive models to enable in-season SY prediction, enabling the development of a framework to integrate phenomics information with powerful ML for prediction enabled prescriptive plant breeding

Cafer Bater'den suluboya sergi

Taha Toros Arşivi, Dosya No: 97-Cafer Bate

Phylogenetic patterns and phenotypic profiles of the species of plants and mammals farmed for food

The origins of agriculture were key events in human history, during which people came to depend for their food on small numbers of animal and plant species. However, the biological traits determining which species were domesticated for food provision, and which were not, are unclear. Here, we investigate the phylogenetic distribution of livestock and crops, and compare their phenotypic traits with those of wild species. Our results indicate that phylogenetic clustering is modest for crop species but more intense for livestock. Domesticated species explore a reduced portion of the phenotypic space occupied by their wild counterparts and have particular traits in common. For example, herbaceous crops are globally characterized by traits including high leaf nitrogen concentration and tall canopies, which make them fast-growing species and proficient competitors. Livestock species are relatively large mammals with low basal metabolic rates, which indicate moderate to slow life histories. Our study therefore reveals ecological differences in domestication potential between plants and mammals. Domesticated plants belong to clades with traits that are advantageous in intensively managed high-resource habitats, whereas domesticated mammals are from clades adapted to moderately productive environments. Combining comparative phylogenetic methods with ecologically relevant traits has proven useful to unravel the causes and consequences of domestication

Plant functional and taxonomic diversity in European grasslands along climatic gradients

Aim: European grassland communities are highly diverse, but patterns and drivers of their continental-scale diversity remain elusive. This study analyses taxonomic and functional richness in European grasslands along continental-scale temperature and precipitation gradients. Location: Europe. Methods: We quantified functional and taxonomic richness of 55,748 vegetation plots. Six plant traits, related to resource acquisition and conservation, were analysed to describe plant community functional composition. Using a null-model approach we derived functional richness effect sizes that indicate higher or lower diversity than expected given the taxonomic richness. We assessed the variation in absolute functional and taxonomic richness and in functional richness effect sizes along gradients of minimum temperature, temperature range, annual precipitation, and precipitation seasonality using a multiple general additive modelling approach. Results: Functional and taxonomic richness was high at intermediate minimum temperatures and wide temperature ranges. Functional and taxonomic richness was low in correspondence with low minimum temperatures or narrow temperature ranges. Functional richness increased and taxonomic richness decreased at higher minimum temperatures and wide annual temperature ranges. Both functional and taxonomic richness decreased with increasing precipitation seasonality and showed a small increase at intermediate annual precipitation. Overall, effect sizes of functional richness were small. However, effect sizes indicated trait divergence at extremely low minimum temperatures and at low annual precipitation with extreme precipitation seasonality. Conclusions: Functional and taxonomic richness of European grassland communities vary considerably over temperature and precipitation gradients. Overall, they follow similar patterns over the climate gradients, except at high minimum temperatures and wide temperature ranges, where functional richness increases and taxonomic richness decreases. This contrasting pattern may trigger new ideas for studies that target specific hypotheses focused on community assembly processes. And though effect sizes were small, they indicate that it may be important to consider climate seasonality in plant diversity studies

sensiPhy: an R-package for sensitivity analysis in phylogenetic comparative methods

1.Biological conclusions drawn from phylogenetic comparative methods can be sensitive to uncertainty in species sampling, phylogeny and data. To be confident about our conclusions, we need to quantify their robustness to such uncertainty. 2.We present sensiPhy, an R-package to easily and rapidly perform sensitivity analysis for phylogenetic comparative methods. sensiPhy allows researchers to evaluate the sampling effort, detect influential species and clades, assess phylogenetic uncertainty and quantify the effects of intraspecific variation, for phylogenetic regression and for metrics of phylogenetic signal, diversification and trait evolution. 3.Uniquely, sensiPhy allows users to simultaneously quantify the effects of different types of uncertainty and potential interactions among them. 4.Using real data, we show how conclusions from comparative methods can be affected by uncertainty and how sensiPhy can help determine if a conclusion is robust. 5.By providing a single, intuitive and user-friendly resource that can evaluate various sources of uncertainty, sensiPhy aims to encourage researchers, and particularly less experienced users, to incorporate sensitivity analyses in their phylogenetic comparative analys

sensiPhy: an R-package for sensitivity analysis in phylogenetic comparative methods

1.Biological conclusions drawn from phylogenetic comparative methods can be sensitive to uncertainty in species sampling, phylogeny and data. To be confident about our conclusions, we need to quantify their robustness to such uncertainty. 2.We present sensiPhy, an R-package to easily and rapidly perform sensitivity analysis for phylogenetic comparative methods. sensiPhy allows researchers to evaluate the sampling effort, detect influential species and clades, assess phylogenetic uncertainty and quantify the effects of intraspecific variation, for phylogenetic regression and for metrics of phylogenetic signal, diversification and trait evolution. 3.Uniquely, sensiPhy allows users to simultaneously quantify the effects of different types of uncertainty and potential interactions among them. 4.Using real data, we show how conclusions from comparative methods can be affected by uncertainty and how sensiPhy can help determine if a conclusion is robust. 5.By providing a single, intuitive and user-friendly resource that can evaluate various sources of uncertainty, sensiPhy aims to encourage researchers, and particularly less experienced users, to incorporate sensitivity analyses in their phylogenetic comparative analys