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We quantify the relationship between forest stand age and fire severity using a detailed case study of Mountain Ash (Eucalyptus regnans Muell) forest burned in south-eastern Australia in 2009. We focused on two important areas of Mountain Ash forest that feature a range of growth stages and disturbance histories. Using probit regression analysis, we identified a strong relationship between the age of a Mountain Ash forest and the severity of damage that the forest sustained from the fires under extreme weather conditions. Stands of Mountain Ash trees between the ages of 7 to 36 years mostly sustained canopy consumption and scorching, which are impacts resulting from high-severity fire. High-severity fire leading to canopy consumption almost never occurred in young stands (<7 years) and also was infrequent in older (>40 years) stands of Mountain Ash. We discuss the significant forest conservation and management implications of these results for Mountain Ash forests as well as other similar biomes, where high-severity fire is a common form of disturbance.

Climate refugia are regions that animals can retreat to, persist in and potentially then expand from under changing environmental conditions. Most forecasts of climate change refugia for species are based on correlative species distribution models (SDMs) using long-term climate averages, projected to future climate scenarios. Limitations of such methods include the need to extrapolate into novel environments and uncertainty regarding the extent to which proximate variables included in the model capture processes driving distribution limits (and thus can be assumed to provide reliable predictions under new conditions). These limitations are well documented; however, their impact on the quality of climate refugia predictions is difficult to quantify. Here, we develop a detailed bioenergetics model for the koala. It indicates that range limits are driven by heat-induced water stress, with the timing of rainfall and heat waves limiting the koala in the warmer parts of its range. We compare refugia predictions from the bioenergetics model with predictions from a suite of competing correlative SDMs under a range of future climate scenarios. SDMs were fitted using combinations of long-term climate and weather extremes variables, to test how well each set of predictions captures the knowledge embedded in the bioenergetics model. Correlative models produced broadly similar predictions to the bioenergetics model across much of the species' current range - with SDMs that included weather extremes showing highest congruence. However, predictions in some regions diverged significantly when projecting to future climates due to the breakdown in correlation between climate variables. We provide unique insight into the mechanisms driving koala distribution and illustrate the importance of subtle relationships between the timing of weather events, particularly rain relative to hot-spells, in driving species-climate relationships and distributions. By unpacking the mechanisms captured by correlative SDMs, we can increase our certainty in forecasts of climate change impacts on species.

The deep ocean is an important component of global biogeochemical cycles because it contains one of the largest pools of reactive carbon and nitrogen on earth. However, the microbial communities that drive deep-sea geochemistry are vastly unexplored. Metatranscriptomics offers new windows into these communities, but it has been hampered by reliance on genome databases for interpretation. We reconstructed the transcriptomes of microbial populations from Guaymas Basin, in the deep Gulf of California, through shotgun sequencing and de novo assembly of total community RNA. Many of the resulting messenger RNA (mRNA) contiguous sequences contain multiple genes, reflecting co-transcription of operons, including those from dominant members. Also prevalent were transcripts with only limited representation (2.8 times coverage) in a corresponding metagenome, including a considerable portion (1.2 Mb total assembled mRNA sequence) with similarity (96%) to a marine heterotroph, Alteromonas macleodii. This Alteromonas and euryarchaeal marine group II populations displayed abundant transcripts from amino-acid transporters, suggesting recycling of organic carbon and nitrogen from amino acids. Also among the most abundant mRNAs were catalytic subunits of the nitrite oxidoreductase complex and electron transfer components involved in nitrite oxidation. These and other novel genes are related to novel Nitrospirae and have limited representation in accompanying metagenomic data. High throughput sequencing of 16S ribosomal RNA (rRNA) genes and rRNA read counts confirmed that Nitrospirae are minor yet widespread members of deep-sea communities. These results implicate a novel bacterial group in deep-sea nitrite oxidation, the second step of nitrification. This study highlights metatranscriptomic assembly as a valuable approach to study microbial communities.

Three-fourths of the recognition-dependent disease resistance genes (R-genes) identified in plants encode nucleotide binding site (NBS) leucine-rich repeat (LRR) proteins. NBS-LRR homologs have only been isolated on a limited scale from sunflower (Helianthus annuus L.), and most of the previously identified homologs are members of two large NBS-LRR clusters harboring downy mildew R-genes. We mined the sunflower EST database and used comparative genomics approaches to develop a deeper understanding of the diversity and distribution of NBS-LRR homologs in the sunflower genome. Collectively, 630 NBS-LRR homologs were identified, 88 by mining a database of 284,241 sunflower ESTs and 542 by sequencing 1,248 genomic DNA amplicons isolated from common and wild sunflower species. DNA markers were developed from 196 unique NBS-LRR sequences and facilitated genetic mapping of 167 NBS-LRR loci. The latter were distributed throughout the sunflower genome in 44 clusters or singletons. Wild species ESTs were a particularly rich source of novel NBS-LRR homologs, many of which were tightly linked to previously mapped downy mildew, rust, and broomrape R-genes. The DNA sequence and mapping resources described here should facilitate the discovery and isolation of recognition-dependent R-genes guarding sunflower from a broad spectrum of economically important diseases. Sunflower nucleotide and amino acid sequences have been deposited in DDBJ/EMBL/GenBank under accession numbers EF 560168-EF 559378 and ABQ 58077-ABQ 57529.