A comprehensive seafloor biomass and abundance database has been constructed from 24 oceanographic institutions worldwide within the Census of Marine Life (CoML) field projects. The machine-learning algorithm, Random Forests, was employed to model and predict seafloor standing stocks from surface primary production, water-column integrated and export particulate organic matter (POM), seafloor relief, and bottom water properties. The predictive models explain 63% to 88% of stock variance among the major size groups. Individual and composite maps of predicted global seafloor biomass and abundance are generated for bacteria, meiofauna, macrofauna, and megafauna (invertebrates and fishes). Patterns of benthic standing stocks were positive functions of surface primary production and delivery of the particulate organic carbon (POC) flux to the seafloor. At a regional scale, the census maps illustrate that integrated biomass is highest at the poles, on continental margins associated with coastal upwelling and with broad zones associated with equatorial divergence. Lowest values are consistently encountered on the central abyssal plains of major ocean basins The shift of biomass dominance groups with depth is shown to be affected by the decrease in average body size rather than abundance, presumably due to decrease in quantity and quality of food supply. This biomass census and associated maps are vital components of mechanistic deep-sea food web models and global carbon cycling, and as such provide fundamental information that can be incorporated into evidence-based management.

Species distribution models (SDMs) have become an essential tool in ecology, biogeography, evolution and, more recently, in conservation biology. How to generalize species distributions in large undersampled areas, especially with few samples, is a fundamental issue of SDMs. In order to explore this issue, we used the best available presence records for the Hooded Crane (Grus monacha, n = 33), White-naped Crane (Grus vipio, n = 40), and Black-necked Crane (Grus nigricollis, n = 75) in China as three case studies, employing four powerful and commonly used machine learning algorithms to map the breeding distributions of the three species: TreeNet (Stochastic Gradient Boosting, Boosted Regression Tree Model), Random Forest, CART (Classification and Regression Tree) and Maxent (Maximum Entropy Models). In addition, we developed an ensemble forecast by averaging predicted probability of the above four models results. Commonly used model performance metrics (Area under ROC (AUC) and true skill statistic (TSS)) were employed to evaluate model accuracy. The latest satellite tracking data and compiled literature data were used as two independent testing datasets to confront model predictions. We found Random Forest demonstrated the best performance for the most assessment method, provided a better model fit to the testing data, and achieved better species range maps for each crane species in undersampled areas. Random Forest has been generally available for more than 20 years and has been known to perform extremely well in ecological predictions. However, while increasingly on the rise, its potential is still widely underused in conservation, (spatial) ecological applications and for inference. Our results show that it informs ecological and biogeographical theories as well as being suitable for conservation applications, specifically when the study area is undersampled. This method helps to save model-selection time and effort, and allows robust and rapid assessments and decisions for efficient conservation.

Despite involvement of large numbers of birds, the delivery rate of Asian-origin viruses to North America through Alaska is apparently low.

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Species distribution models (SDMs) are widely used to predict and study distributions of species. Many different modeling methods and associated algorithms are used and continue to emerge. It is important to understand how different approaches perform, particularly when applied to species occurrence records that were not gathered in struc­tured surveys (e.g. opportunistic records). This need motivated a large-scale, collaborative effort, published in 2006, that aimed to create objective comparisons of algorithm performance. As a benchmark, and to facilitate future comparisons of approaches, here we publish that dataset: point location records for 226 anonymized species from six regions of the world, with accompanying predictor variables in raster (grid) and point formats. A particularly interesting characteristic of this dataset is that independent presence-absence survey data are available for evaluation alongside the presence-only species occurrence data intended for modeling. The dataset is available on Open Science Framework and as an R package and can be used as a benchmark for modeling approaches and for testing new ways to evaluate the accuracy of SDMs.

Summary 1.Climate is an important factor influencing the population dynamics of large herbivores operating directly on individuals or through its effect on forage characteristics. However, the seasonal effect of climate may differ between forage-and predator-limited populations because of a climatic influence on predation rates. The influence of climate on predator-limited large herbivores is less well known than on forage-limited populations. Further, the effect of Pacific-based climate on large herbivore populations has been rarely assessed. 2. We investigated the effect of the Pacific Decadal Oscillation (PDO), across different seasons, on recruitment in 10 populations (herds) of mountain-dwelling caribou Rangifer tarandus caribou L. in the Yukon Territory, Canada. These low-density populations occur in highly seasonal environments and are considered predator-limited with high neonatal calf mortality. Hence, in most years females do not spend resources through lactational support during the summer and resource intake is devoted to self-maintenance. We predicted that climate affecting environmental conditions at calving would have a strong effect on recruitment via its influence on predation rates. We also predicted that climatic conditions prior to conception could have an effect on recruitment through its influence on female fecundity. We modelled recruitment (n = 165) by seasonal PDO values using generalized linear mixed-effects models with herd-varying coefficients. 3. We found that recruitment variability was best explained by variation in winter climate (b = 0AE110, SE = 0AE007) prior to birth (in utero) and May climate (b = 0AE013, SE = 0AE006) at calving. There was little support for a pre-conception climate effect influencing female body condition and hence fecundity. These results confirm that recruitment in these populations is limited by predation and that forage-limitation is not a significant factor in their population dynamics. There was considerable variability in herd-specific relationships between the PDO and recruitment. Incorporating herd-specific characteristics, such as variable predator densities or terrain characteristics within a herd range, may shed greater light on the complex relationship between climate and ungulate population dynamics.