Background: Determining an absolute timescale for avian evolutionary history has proven contentious. The two sources of information available, paleontological data and inference from extant molecular genetic sequences (colloquially, 'rocks' and 'clocks'), have appeared irreconcilable; the fossil record supports a Cenozoic origin for most modern lineages, whereas molecular genetic estimates suggest that these same lineages originated deep within the Cretaceous and survived the K-Pg (Cretaceous-Paleogene; formerly Cretaceous-Tertiary or K-T) mass-extinction event. These two sources of data therefore appear to support fundamentally different models of avian evolution. The paradox has been speculated to reflect deficiencies in the fossil record, unrecognized biases in the treatment of genetic data or both. Here we attempt to explore uncertainty and limit bias entering into molecular divergence time estimates through: (i) improved taxon (n = 135) and character (n = 4594 bp mtDNA) sampling; (ii) inclusion of multiple cladistically tested internal fossil calibration points (n = 18); (iii) correction for lineage-specific rate heterogeneity using a variety of methods (n = 5); (iv) accommodation of uncertainty in tree topology; and (v) testing for possible effects of episodic evolution.

The world's most endangered canid is the Ethiopian wolf Canis simensis, which is found in six isolated areas of the Ethiopian highlands with a total population of no more than 500 individuals. Ethiopian wolf populations are declining due to habitat loss and extermination by humans. Moreover, in at least one population, Ethiopian wolves are sympatric with domestic dogs, which may hybridize with them, compete for food, and act as disease vectors. Using molecular techniques, we address four questions concerning Ethiopian wolves that have conservation implications. First, we determine the relationships of Ethiopian wolves to other wolf-like canids by phylogenetic analysis of 2001 base pairs of mitochondrial DNA (mtDNA) sequence. Our results suggest that the Ethiopian wolf is a distinct species more closely related to gray wolves and coyotes than to any African canid. The mtDNA sequence similarity with gray wolves implies that the Ethiopian wolf may hybridize with domestic dogs, a recent derivative of the gray wolf. We examine this possibility through mtDNA restriction fragment analysis and analysis of nine microsatellite loci in populations of Ethiopian wolves. The results imply that hybridization has occurred between female Ethiopian wolves and male domestic dogs in one population. Finally, we assess levels of variability within and between two Ethiopian wolf populations. Although these closely situated populations are not differentiated, the level of variability in both is low, suggesting long-term effective population sizes of less than a few hundred individuals. We recommend immediate captive breeding of Ethiopian wolves to protect their gene pool from dilution and further loss of genetic variability.

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance Manuscript spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplank-ton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the shortwave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radio-metric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration <2%, relative calibration of 0.2%, polarization sensitivity <1%, high radiometric stability and linearity, and operations designed to minimize sunglint; and (4) temporal resolution of hours to days. We refer to these combined specifications as H4 imaging. Enabling H4 imaging is vital for the conservation and management of global biodiversity and ecosystem services, including food provisioning and water security. An agile satellite in a 3-d repeat low-Earth orbit could sample 30-km swath images of several hundred coastal habitats daily. Nine H4 satellites would provide weekly coverage of global coastal zones. Such satellite constellations are now feasible and are used in various applications.

No abstract

Human-driven global change is causing ongoing declines in biodiversity worldwide. In order to address these declines, decision-makers need accurate assessments of the status of and pressures on biodiversity. However, these are heavily constrained by incomplete and uneven spatial, temporal and taxonomic coverage. For instance, data from regions such as Europe and North America are currently used overwhelmingly for large-scale biodiversity assessments due to lesser availability of suitable data from other, more biodiversity-rich, regions. These data-poor regions are often those experiencing the strongest threats to biodiversity, however. There is therefore an urgent need to fill the existing gaps in global biodiversity monitoring. Here, we review current knowledge on best practice in capacity building for biodiversity monitoring and provide an overview of existing means to improve biodiversity data collection considering the different types of biodiversity monitoring data. Our review comprises insights from work in Africa, South America, Polar Regions and Europe; in governmentfunded, volunteer and citizen-based monitoring in terrestrial, freshwater and marine ecosystems. The key steps to effectively building capacity in biodiversity monitoring are: identifying monitoring questions and aims; identifying the key components, functions, and processes to monitor; identifying the most suitable monitoring methods for these elements, carrying out monitoring activities; managing the resultant data; and interpreting monitoring data. Additionally, biodiversity monitoring should use multiple approaches including extensive and intensive monitoring through volunteers and professional scientists but also harnessing new technologies. Finally, we call on the scientific community to share biodiversity monitoring data, knowledge and tools to ensure the accessibility, interoperability, and reporting of biodiversity data at a global scale.4