Effect of local climate anomalies on giraffe survival

With the rapid pace of global warming, there is an urgent need to understand survival responses to climate, particularly for large mammals that are already experiencing population declines associated with anthropogenic pressures such as poaching and habitat loss. We tested hypotheses about the interactive effects of local climatic anomalies (variations around a long-term mean) and proximity to edge of protected area boundaries on seasonal adult and juvenile survival in a population of 2,385 individually identified giraffes monitored over 8 years in the Tarangire Ecosystem of northern Tanzania. Temperature anomalies were positively correlated with seasonal survival of adult giraffes, suggesting these megaherbivores are adapted to hot conditions. Higher seasonal rainfall anomalies were negatively correlated with both juvenile and adult survival, and greater vegetation greenness was associated with lower adult survival. During seasons of anomalously high rainfall and vegetation greenness, higher parasite and disease abundance, poorer-quality nutrition in forage, and higher predation risk may all play a role in lowering giraffe survival. Furthermore, climate-associated reduction in survival was most pronounced during the short rainy season for adult giraffes living closer to the edge of protected areas, indicating that the influence of climate anomalies may be exacerbated by anthropogenic edge effects such as poaching or livestock keeping. Precipitation in East Africa is projected to increase substantially, with a greater proportion of rain falling during heavy events in the short rainy season, which may threaten persistence of giraffes in one of Earth’s most important landscapes for large mammals

Modeling adaptive and non-adaptive responses to environmental change

Understanding how the natural world will be impacted by environmental change over the coming decades is one of the most pressing challenges facing humanity. Addressing this challenge is difficult because environmental change can generate both population level plastic and evolutionary responses, with plastic responses being either adaptive or non-adaptive. We develop an approach that links quantitative genetic theory with data-driven structured models to allow prediction of population responses to environmental change via plasticity and adaptive evolution. After introducing general new theory, we construct a number of example models to demonstrate that evolutionary responses to environmental change over the short-term will be considerably slower than plastic responses, and that the rate of adaptive evolution to a new environment depends upon whether plastic responses are adaptive or non-adaptive. Parameterization of the models we develop requires information on genetic and phenotypic variation and demography that will not always be available, meaning that simpler models will often be required to predict responses to environmental change. We consequently develop a method to examine whether the full machinery of the evolutionarily explicit models we develop will be needed to predict responses to environmental change, or whether simpler non-evolutionary models that are now widely constructed may be sufficient

Regulatory feedback response mechanisms to phosphate starvation in rice

Phosphorus is a growth-limiting nutrient for plants. The growing scarcity of phosphate stocks threatens global food security. Phosphate-uptake regulation is so complex and incompletely known that attempts to improve phosphorus use efficiency have had extremely limited success. This study improves our understanding of the molecular mechanisms underlying phosphate uptake by investigating the transcriptional dynamics of two regulators: the Ubiquitin ligase PHO2 and the long non-coding RNA IPS1. Temporal measurements of RNA levels have been integrated into mechanistic mathematical models using advanced statistical techniques. Models based solely on current knowledge could not adequately explain the temporal expression profiles. Further modeling and bioinformatics analysis have led to the prediction of three regulatory features: the PHO2 protein mediates the degradation of its own transcriptional activator to maintain constant PHO2 mRNA levels; the binding affinity of the transcriptional activator of PHO2 is impaired by a phosphate-sensitive transcriptional repressor/inhibitor; and the extremely high levels of IPS1 and its rapid disappearance upon Pi re-supply are best explained by Pi-sensitive RNA protection. This work offers both new opportunities for plant phosphate research that will be essential for informing the development of phosphate efficient crop varieties, and a foundation for the development of models integrating phosphate with other stress responses

Adaptive responses of animals to climate change are most likely insufficient

Biological responses to climate change have been widely documented across taxa and regions, but it remains unclear whether species are maintaining a good match between phenotype and environment, i.e. whether observed trait changes are adaptive. Here we reviewed 10,090 abstracts and extracted data from 71 studies reported in 58 relevant publications, to assess quantitatively whether phenotypic trait changes associated with climate change are adaptive in animals. A meta-analysis focussing on birds, the taxon best represented in our dataset, suggests that global warming has not systematically affected morphological traits, but has advanced phenological traits. We demonstrate that these advances are adaptive for some species, but imperfect as evidenced by the observed consistent selection for earlier timing. Application of a theoretical model indicates that the evolutionary load imposed by incomplete adaptive responses to ongoing climate change may already be threatening the persistence of species. © 2019, The Author(s)

The ecological forecast horizon, and examples of its uses and determinants

Forecasts of ecological dynamics in changing environments are increasingly important, and are available for a plethora of variables, such as species abundance and distribution, community structure and ecosystem processes. There is, however, a general absence of knowledge about how far into the future, or other dimensions (space, temperature, phylogenetic distance), useful ecological forecasts can be made, and about how features of ecological systems relate to these distances. The ecological forecast horizon is the dimensional distance for which useful forecasts can be made. Five case studies illustrate the influence of various sources of uncertainty (e.g. parameter uncertainty, environmental variation, demographic stochasticity and evolution), level of ecological organisation (e.g. population or community), and organismal properties (e.g. body size or number of trophic links) on temporal, spatial and phylogenetic forecast horizons. Insights from these case studies demonstrate that the ecological forecast horizon is a flexible and powerful tool for researching and communicating ecological predictability. It also has potential for motivating and guiding agenda setting for ecological forecasting research and development

Polyploidization Altered Gene Functions in Cotton (Gossypium spp.)

Cotton (Gossypium spp.) is an important crop plant that is widely grown to produce both natural textile fibers and cottonseed oil. Cotton fibers, the economically more important product of the cotton plant, are seed trichomes derived from individual cells of the epidermal layer of the seed coat. It has been known for a long time that large numbers of genes determine the development of cotton fiber, and more recently it has been determined that these genes are distributed across At and Dt subgenomes of tetraploid AD cottons. In the present study, the organization and evolution of the fiber development genes were investigated through the construction of an integrated genetic and physical map of fiber development genes whose functions have been verified and confirmed. A total of 535 cotton fiber development genes, including 103 fiber transcription factors, 259 fiber development genes, and 173 SSR-contained fiber ESTs, were analyzed at the subgenome level. A total of 499 fiber related contigs were selected and assembled. Together these contigs covered about 151 Mb in physical length, or about 6.7% of the tetraploid cotton genome. Among the 499 contigs, 397 were anchored onto individual chromosomes. Results from our studies on the distribution patterns of the fiber development genes and transcription factors between the At and Dt subgenomes showed that more transcription factors were from Dt subgenome than At, whereas more fiber development genes were from At subgenome than Dt. Combining our mapping results with previous reports that more fiber QTLs were mapped in Dt subgenome than At subgenome, the results suggested a new functional hypothesis for tetraploid cotton. After the merging of the two diploid Gossypium genomes, the At subgenome has provided most of the genes for fiber development, because it continues to function similar to its fiber producing diploid A genome ancestor. On the other hand, the Dt subgenome, with its non-fiber producing D genome ancestor, provides more transcription factors that regulate the expression of the fiber genes in the At subgenome. This hypothesis would explain previously published mapping results. At the same time, this integrated map of fiber development genes would provide a framework to clone individual full-length fiber genes, to elucidate the physiological mechanisms of the fiber differentiation, elongation, and maturation, and to systematically study the functional network of these genes that interact during the process of fiber development in the tetraploid cottons

Molecular Mechanisms of Fiber Differential Development between G. barbadense and G. hirsutum Revealed by Genetical Genomics

Cotton fiber qualities including length, strength and fineness are known to be controlled by genes affecting cell elongation and secondary cell wall (SCW) biosynthesis, but the molecular mechanisms that govern development of fiber traits are largely unknown. Here, we evaluated an interspecific backcrossed population from G. barbadense cv. Hai7124 and G. hirsutum acc. TM-1 for fiber characteristics in four-year environments under field conditions, and detected 12 quantitative trait loci (QTL) and QTL-by-environment interactions by multi-QTL joint analysis. Further analysis of fiber growth and gene expression between TM-1 and Hai7124 showed greater differences at 10 and 25 days post-anthesis (DPA). In this two period important for fiber performances, we integrated genome-wide expression profiling with linkage analysis using the same genetic materials and identified in total 916 expression QTL (eQTL) significantly (P<0.05) affecting the expression of 394 differential genes. Many positional cis-/trans-acting eQTL and eQTL hotspots were detected across the genome. By comparative mapping of eQTL and fiber QTL, a dataset of candidate genes affecting fiber qualities was generated. Real-time quantitative RT-PCR (qRT-PCR) analysis confirmed the major differential genes regulating fiber cell elongation or SCW synthesis. These data collectively support molecular mechanism for G. hirsutum and G. barbadense through differential gene regulation causing difference of fiber qualities. The down-regulated expression of abscisic acid (ABA) and ethylene signaling pathway genes and high-level and long-term expression of positive regulators including auxin and cell wall enzyme genes for fiber cell elongation at the fiber developmental transition stage may account for superior fiber qualities

Guidelines for Genome-Scale Analysis of Biological Rhythms

Genome biology approaches have made enormous contributions to our understanding of biological rhythms, particularly in identifying outputs of the clock, including RNAs, proteins, and metabolites, whose abundance oscillates throughout the day. These methods hold significant promise for future discovery, particularly when combined with computational modeling. However, genome-scale experiments are costly and laborious, yielding “big data” that are conceptually and statistically difficult to analyze. There is no obvious consensus regarding design or analysis. Here we discuss the relevant technical considerations to generate reproducible, statistically sound, and broadly useful genome-scale data. Rather than suggest a set of rigid rules, we aim to codify principles by which investigators, reviewers, and readers of the primary literature can evaluate the suitability of different experimental designs for measuring different aspects of biological rhythms. We introduce CircaInSilico, a web-based application for generating synthetic genome biology data to benchmark statistical methods for studying biological rhythms. Finally, we discuss several unmet analytical needs, including applications to clinical medicine, and suggest productive avenues to address them