Based on the sensitivity of species to ongoing climate change, and numerous challenges they face tracking suitable conditions, there is growing interest in species' capacity to adapt to climatic stress. Here, we develop and apply a new generic modelling approach (AdaptR) that incorporates adaptive capacity through physiological limits, phenotypic plasticity, evolutionary adaptation and dispersal into a species distribution modelling framework. Using AdaptR to predict change in the distribution of 17 species of Australian fruit flies (Drosophilidae), we show that accounting for adaptive capacity reduces projected range losses by up to 33% by 2105. We identify where local adaptation is likely to occur and apply sensitivity analyses to identify the critical factors of interest when parameters are uncertain. Our study suggests some species could be less vulnerable than previously thought, and indicates that spatiotemporal adaptive models could help improve management interventions that support increased species' resilience to climate change.

Preface 54There is much interest in using Earth Observation (EO) technology to track biodiversity, 55 ecosystem functions, and ecosystem services, understandable given the fast pace of 56 biodiversity loss. However, because most biodiversity is invisible to EO, EO-based 57 indicators could be misleading, which can reduce the effectiveness of nature 58 conservation and even unintentionally decrease conservation effort. We describe an 59 approach that combines automated recording devices, high-throughput DNA Meeting the Aichi Biodiversity Targets 64From Google Earth to airborne sensors, the Copernicus Sentinels, and cube satellites, 65Earth Observation is undergoing a rapid expansion in capacity, accessibility, resolution, 66and signal-to-noise ratio, resulting in a recognised shift in our capability for using 67 remote-sensing technologies to monitor biophysical processes on land and water [1][2][3] . 68These advances are motivating calls to use Earth Observation products to manage our 69 natural environment and to track progress toward global and national policy targets on 70 biodiversity and ecosystem services [4][5][6] . Foremost among these policies are the Strategic 71Plan for Biodiversity and the Aichi Biodiversity Targets, which were adopted in 2010 by products (net primary productivity and fire incidence) that could serve as Essential 108Biodiversity Variables for the Sahara, despite this biome's suitability for remote sensing 109 due to its visible biodiversity hotspots, remoteness, and availability of long time series. 110Many of the Aichi Targets require data with species-level resolution, either because some 111 species are direct policy targets (e.g. Target 9: "invasive species controlled or eradicated") 112 or because species compositional data define the metric (e.g. Target 11: "protected areas 113 are ecologically representative and conserved effectively"). species, but information could be 'borrowed' from data-rich species to increase the 294 precision of predictions for rare species. These procedures were able to compensate for 295 the fact that only 134 total bird species had been detected in the survey, which is less The GDM was parameterised with a training dataset of 2280 surveys and fourteen 303 environmental variables and explained 57% of the variation in beta diversity. In addition, for linking pure-EO data to biodiversity. 382The major remaining components of uncertainty relate to generalisability, because only a 383 single FSC-certified reserve was sampled; the applicability of results to arboreal species, 384 which tend to be detected more frequently in forests with disturbed canopy but are not 385 necessarily more widespread in these forests; and wide confidence intervals around 386 parameter estimates for some species as a consequence of sparse data and a fairly 394Another example of the CEOBE approach is the use of Generalised Dissimilarity 395Modelling to connect EO-derived metrics of habitat degradation and fragmentation 89,90 396 to over 300 million records of more ...

Adnectins are targeted biologics derived from the tenth type III domain of human fibronectin (¹⁰Fn3), a member of the immunoglobulin superfamily. Target-specific binders are selected from libraries generated by diversifying the three ¹⁰Fn3 loops that are analogous to the complementarity determining regions of antibodies. The crystal structures of two Adnectins were determined, each in complex with its therapeutic target, EGFR or IL-23. Both Adnectins bind different epitopes than those bound by known monoclonal antibodies. Molecular modeling suggests that some of these epitopes might not be accessible to antibodies because of the size and concave shape of the antibody combining site. In addition to interactions from the Adnectin diversified loops, residues from the N terminus and/or the β strands interact with the target proteins in both complexes. Alanine-scanning mutagenesis confirmed the calculated binding energies of these β strand interactions, indicating that these nonloop residues can expand the available binding footprint.

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Diversity and abundance of ammonia-oxidizing Betaproteobacteria (␤-AOB) and archaea (AOA) were investigated in a New England salt marsh at sites dominated by short or tall Spartina alterniflora (SAS and SAT sites, respectively) or Spartina patens (SP site). AOA amoA gene richness was higher than ␤-AOB amoA richness at SAT and SP, but AOA and ␤-AOB richness were similar at SAS. ␤-AOB amoA clone libraries were composed exclusively of Nitrosospira-like amoA genes. AOA amoA genes at SAT and SP were equally distributed between the water column/sediment and soil/sediment clades, while AOA amoA sequences at SAS were primarily affiliated with the water column/sediment clade. At all three site types, AOA were always more abundant than ␤-AOB based on quantitative PCR of amoA genes. At some sites, we detected 10 9 AOA amoA gene copies g of sediment ؊1 . Ratios of AOA to ␤-AOB varied over 2 orders of magnitude among sites and sampling dates. Nevertheless, abundances of AOA and ␤-AOB amoA genes were highly correlated. Abundance of 16S rRNA genes affiliated with Nitrosopumilus maritimus, Crenarchaeota group I.1b, and pSL12 were positively correlated with AOA amoA abundance, but ratios of amoA to 16S rRNA genes varied among sites. We also observed a significant effect of pH on AOA abundance and a significant salinity effect on both AOA and ␤-〈⌷〉 abundance. Our results expand the distribution of AOA to salt marshes, and the high numbers of AOA at some sites suggest that salt marsh sediments serve as an important habitat for AOA.Nitrification, the sequential oxidation of ammonia to nitrite and nitrate, is a critical step in the nitrogen cycle and is mediated by a suite of phylogenetically and physiologically distinct microorganisms. The recent discovery of ammonia oxidation among Archaea (17, 38) has led to a dramatic shift in the current model of nitrification and to new questions of niche differentiation between putative ammonia-oxidizing Archaea (AOA) and the more-well-studied ammonia-oxidizing Betaproteobacteria (␤-AOB). Based on surveys of 16S rRNA genes and archaeal amoA genes, it is evident that AOA occupy a wide range of niches (10), suggesting a physiologically diverse group of Archaea. Additionally, in studies where AOA and ␤-AOB were both targeted, AOA were typically more abundant than their bacterial counterparts (19,21,42). However, there are reports of ␤-AOB outnumbering AOA in estuarine systems (6, 33), suggesting a possible shift in competitive dominance under certain conditions.Patterns of ␤-AOB diversity in estuaries have been well characterized and appear to be regulated by similar mechanisms within geographically disparate systems (4, 11, 32). However, AOA distribution and their role in nitrification relative to ␤-AOB remain to be determined. A few studies have begun to address this question in different estuaries, but no unifying patterns or mechanisms have emerged. Although ␤-AOB have been well studied along estuarine salinity gradients (1,3,4,7,11,13,22,33,39) and recent studies have begun to address AOA ...

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Summary Climate extremes and their physical impacts – including droughts, fires, floods, heat waves, storm surges and tropical cyclones – are important structuring forces in riverine ecosystems. Climate change is expected to increase the future occurrence of extremes, with potentially devastating effects on rivers and streams. We synthesise knowledge of extremes and their impacts on riverine ecosystems in Australia, a country for which projected changes in event characteristics reflect global trends. Hydrologic extremes play a major structuring role in river ecology across Australia. Droughts alter water quality and reduce habitat availability, driving organisms to refugia. Extreme floods increase hydrological connectivity and trigger booms in productivity, but can also alter channel morphology and cause disturbances such as hypoxic blackwater events. Tropical cyclones and post‐cyclonic floods damage riparian vegetation, erode stream banks and alter water quality. Cyclone‐induced delivery of large woody debris provides important instream habitat, although the wider ecological consequences of tropical cyclones are uncertain. Wildfires destroy catchment vegetation and expose soils, increasing inputs of fine sediment and nutrients to streams, particularly when followed by heavy rains. Research on the impacts of heat waves and storm surges is scarce, but data on temperature and salinity tolerances, respectively, may provide some insight into ecological responses. We identify research gaps and hypotheses to guide future research on the ecology of extreme climate events in Australia and beyond. A range of phenomenological, experimental and modelling approaches is needed to develop a mechanistic understanding of the ecological impact of extreme events and inform prediction of responses to future change.

Land‐use change is one of the biggest threats to biodiversity globally. The effects of land use on biodiversity manifest primarily at local scales which are not captured by the coarse spatial grain of current global land‐use mapping. Assessments of land‐use impacts on biodiversity across large spatial extents require data at a similar spatial grain to the ecological processes they are assessing. Here, we develop a method for statistically downscaling mapped land‐use data that combines generalized additive modeling and constrained optimization. This method was applied to the 0.5° Land‐use Harmonization data for the year 2005 to produce global 30″ (approx. 1 km2) estimates of five land‐use classes: primary habitat, secondary habitat, cropland, pasture, and urban. The original dataset was partitioned into 61 bio‐realms (unique combinations of biome and biogeographical realm) and downscaled using relationships with fine‐grained climate, land cover, landform, and anthropogenic influence layers. The downscaled land‐use data were validated using the PREDICTS database and the geoWiki global cropland dataset. Application of the new method to all 61 bio‐realms produced global fine‐grained layers from the 2005 time step of the Land‐use Harmonization dataset. Coarse‐scaled proportions of land use estimated from these data compared well with those estimated in the original datasets (mean R 2: 0.68 ± 0.19). Validation with the PREDICTS database showed the new downscaled land‐use layers improved discrimination of all five classes at PREDICTS sites (P < 0.0001 in all cases). Additional validation of the downscaled cropping layer with the geoWiki layer showed an R 2 improvement of 0.12 compared with the Land‐use Harmonization data. The downscaling method presented here produced the first global land‐use dataset at a spatial grain relevant to ecological processes that drive changes in biodiversity over space and time. Integrating these data with biodiversity measures will enable the reporting of land‐use impacts on biodiversity at a finer resolution than previously possible. Furthermore, the general method presented here could be useful to others wishing to downscale similarly constrained coarse‐resolution data for other environmental variables.