A super SDM (species distribution model) 'in the cloud' for better habitat-association inference with a 'big data' application of the Great Gray Owl for Alaska.

The currently available distribution and range maps for the Great Grey Owl (GGOW; Strix nebulosa) are ambiguous, contradictory, imprecise, outdated, often hand-drawn and thus not quantified, not based on data or scientific. In this study, we present a proof of concept with a biological application for technical and biological workflow progress on latest global open access 'Big Data' sharing, Open-source methods of R and geographic information systems (OGIS and QGIS) assessed with six recent multi-evidence citizen-science sightings of the GGOW. This proposed workflow can be applied for quantified inference for any species-habitat model such as typically applied with species distribution models (SDMs). Using Random Forest-an ensemble-type model of Machine Learning following Leo Breiman's approach of inference from predictions-we present a Super SDM for GGOWs in Alaska running on Oracle Cloud Infrastructure (OCI). These Super SDMs were based on best publicly available data (410 occurrences + 1% new assessment sightings) and over 100 environmental GIS habitat predictors ('Big Data'). The compiled global open access data and the associated workflow overcome for the first time the limitations of traditionally used PC and laptops. It breaks new ground and has real-world implications for conservation and land management for GGOW, for Alaska, and for other species worldwide as a 'new' baseline. As this research field remains dynamic, Super SDMs can have limits, are not the ultimate and final statement on species-habitat associations yet, but they summarize all publicly available data and information on a topic in a quantified and testable fashion allowing fine-tuning and improvements as needed. At minimum, they allow for low-cost rapid assessment and a great leap forward to be more ecological and inclusive of all information at-hand. Using GGOWs, here we aim to correct the perception of this species towards a more inclusive, holistic, and scientifically correct assessment of this urban-adapted owl in the Anthropocene, rather than a mysterious wilderness-inhabiting species (aka 'Phantom of the North'). Such a Super SDM was never created for any bird species before and opens new perspectives for impact assessment policy and global sustainability.

Epidemiological Characterization of African Swine Fever Dynamics in Ukraine, 2012-2023.

African swine fever (ASF) is a viral disease, endemic to Africa, that causes high mortality when introduced into domestic pig populations. Since the emergence of p72-genotype II African swine fever virus (ASFV) in Georgia in 2007, an ASF epidemic has been spreading across Europe and many countries in Asia. The epidemic first reached Ukraine in 2012. To better understand the dynamics of spread of ASF in Ukraine, we analyzed spatial and temporal outbreak data reported in Ukraine between 2012 and mid-2023. The highest numbers of outbreaks were reported in 2017 (N = 163) and 2018 (N = 145), with overall peak numbers of ASF outbreaks reported in August (domestic pigs) and January (wild boars). While cases were reported from most of Ukraine, we found a directional spread from the eastern and northern borders towards the western and southern regions of Ukraine. Many of the early outbreaks (before 2016) were adjacent to the border, which is again true for more recent outbreaks in wild boar, but not for recent outbreaks in domestic pigs. Outbreaks prior to 2016 also occurred predominantly in areas with a below average domestic pig density. This new analysis suggests that wild boars may have played an important role in the introduction and early spread of ASF in Ukraine. However, in later years, the dynamic suggests human activity as the predominant driver of spread and a separation of ASF epizootics between domestic pigs and in wild boars. The decline in outbreaks since 2019 suggests that the implemented mitigation strategies are effective, even though long-term control or eradication remain challenging and will require continued intensive surveillance of ASF outbreak patterns.

Rabies Elimination: Is It Feasible without Considering Wildlife?

Rabies is a vaccine-preventable fatal viral disease that is zoonotic in nature. In this article, we provide a justification why the agreement of the World Health Organization (WHO), the Food and Agriculture Organization (FAO), the World Organization for Animal Health (OIE), and Global Alliance for Rabies Control (GARC) on The Global Strategic Plan to End Human Deaths from Dog-mediated Rabies by 2030 should also include a more holistic approach and ecologic views.

Herbivore species coexistence in changing rangeland ecosystems: First high resolution national open-source and open-access ensemble models for Iceland.

Rangeland ecosystems are changing worldwide with the abandonment of extensive pastoralism practices and greater interest for species coexistence. However, the lack of compiled data on current changes in the abundance and distribution of herbivores challenges rangeland management decisions. Here we gathered and made available for the first time the most extensive set of occurrence data for rangeland herbivores in Iceland in an Open Access framework for transparent and repeatable science-based decisions. We mapped fine scale species distribution overlap to identify areas at risk for wildlife-livestock conflict and overgrazing. Nationwide and long term (1861-2021) occurrence data from 8 independent datasets were used alongside 11 predictor raster layers ("Big Data") to data mine and map the distribution of the domestic sheep (Ovis aries), feral reindeer (Rangifer tarandus tarandus), pink-footed geese (Anser brachyrhynchus), and rock ptarmigan (Lagopus muta islandorum) over the country during the summer. Using algorithms of Maxent in R, RandomForest, TreeNet (stochastic gradient boosting) and MARS (Splines) in Minitab-SPM 8.3, we computed 1 km pixel predictions from machine learning-based ensemble models. Our high-resolution models were tested with alternative datasets, and Area Under the Curve (AUC) values that indicated good (reindeer: 0.8817 and rock ptarmigan: 0.8844) to high model accuracy (sheep: 0.9708 and pink-footed goose: 0.9143). Whenever possible, source data and models are made available online and described with ISO-compliant metadata. Our results illustrate that sheep and pink-footed geese have the greatest overlap in distribution with potential implication for wildlife-livestock conflicts and continued ecosystem degradation even under diminishing livestock abundance at higher elevation. These nationwide models and data are a global asset and a first step in making available the best data for science-based sustainable decision-making about national herbivores affecting species coexistence and environmental management.

Effects of climate and human activity on the current distribution of amphibians in China.

In China, as elsewhere, amphibians are highly endangered. Anthropogenic environmental change has affected the distribution and population dynamics of species, and species distributions at a broad scale are strongly driven by climate and species' ability to disperse. Yet, current knowledge remains limited on how widespread human activity affects the distribution patterns of amphibians in China and whether this effect extends beyond climate. We compiled a relatively comprehensive database on the distribution of 196 amphibian species in China from the literature, public databases, and field data. We obtained 25,826 records on almost 50% of known species in China. To test how environmental factors and human activities influence the current distribution of amphibians (1960-1990), we used range filling, which is species realized ranges relative to their potential climate distribution. We used all species occurrence records to represent realized range and niche models to predict potential distribution range. To reduce uncertainty, we used 3 regression methods (beta regression, generalized boosted regression models, and random forest) to test the associations of species range filling with human activity, climate, topography, and range size. The results of the 3 approaches were consistent. At the species level, mean annual precipitation (climate) had the most effect on spatial distribution pattern of amphibians in China, followed by range size. Human activity ranked last. At the spatial level, mean annual precipitation remained the most important factor. Regions in southeastern of China that are currently moist supported the highest amphibian diversity, but were predicted to experience a decline in precipitation under climate change scenarios. Consequently, the distributions of amphibians will likely shift to the northwest in the future, which could affect future conservation efforts.

En China, como en todos lados, los anfibios están gravemente en peligro. El cambio ambiental antropogénico ha afectado la distribución y dinámica poblacional de especies, y la distribución de especies a gran escala están muy influidas por el clima y la habilidad de dispersión de las especies. Sin embargo, el conocimiento actual sigue siendo limitado sobre cómo la actividad humana generalizada afecta a los patrones de distribución de anfibios en China y si este efecto se extiende más allá del clima. A partir de literatura, bases de datos públicas y datos de campo, integramos una base datos relativamente completa sobre la distribución de 196 especies de anfibios en China. Obtuvimos 25,826 registros de casi 50% de las especies conocidas en China. Para probar cómo los factores ambientales y las actividades humanas influyen en la distribución actual de anfibios (1960-1990), utilizamos la ocupación de rango, que contrasta los rangos de distribución observada de las especies en relación con su distribución climática potencial. Utilizamos los registros de ocurrencia de todas las especies para representar el rango observado y modelos de nicho para predecir el rango de distribución potencial. Para reducir la incertidumbre, utilizamos 3 métodos de regresión (regresión beta, modelos de regresión acelerada generalizada y bosque aleatorio) para probar las asociaciones de la ocupación de rango de especies con la actividad humana, clima, topografía y extensión de rango. Los resultados de los tres métodos fueron consistentes. A nivel de especie, la precipitación media anual (clima) tuvo el mayor efecto sobre el patrón de distribución de anfibios en China, seguida por la extensión del rango. La actividad humana ocupó el último lugar. A nivel espacial, la precipitación media anual siguió como el factor más importante. Las regiones en el sureste de China que aun son húmedas sostuvieron la mayor diversidad de anfibios, pero se pronosticó que la precipitación declinará bajo escenarios de cambio climático. Consecuentemente, la distribución de anfibios muy probablemente cambiará hacia el noreste, lo cual podría afectar esfuerzos futuros de conservación.

Time and energy minimization strategy codetermine the loop migration of demoiselle cranes around the Himalayas.

Tens of thousands of demoiselle cranes' crossing the Himalayas to the Indian subcontinent have been reported for decades, but their exact spring migration route remained a mystery until our previous study found they made a detour in spring along the western edge of the Himalayas and crossed the Mongolian Plateau to their breeding areas based on satellite telemetry of 3 birds. To corroborate the loop migration pattern and explore whether demoiselle crane's loop migration route is shaped by time- and energy-minimization strategies in spring and autumn and how the temporal and spatial variation of environmental conditions contribute to crane's selection of migration routes, we tracked 11 satellite-tagged demoiselle cranes from their breeding area in China and Russia, simulated 2 pseudo migration routes, and then compared the environmental conditions, time, and energy cost between true and pseudo routes in the same season. Results show that demoiselles' spring migration obeyed time-minimization hypothesis, avoiding the colder Qinghai-Tibet Plateau, benefited by abundant food and higher thermal and orographic uplift along the route; autumn migration follows energy-minimization hypothesis with the shorter route. Our research will contribute to uncover the mechanical reasons why demoiselle crane avoids crossing the giant barrier of the Himalayas in spring, and shapes a loop migration route.

Where should China practice forestry in a warming world?

As a nature-based and cost-effective solution, forestation plays a crucial role in combating global warming, biodiversity collapse, environmental degradation, and global well-being. Although China is acknowledged as a global leader of forestation and has achieved considerable overall success in environmental improvements through mega-forestation programs, many negative effects have also emerged at local scales due to the planting of maladapted tree species. To better help achieve carbon neutrality and the new vision of an ecological civilization, China has committed to further increase forestation. However, where forestation lands and such efforts should really be located is not so well understood yet and agreed upon, especially in the face of rapid climate change. Based on an ensemble-learning machine, we predicted the spatial habitats (ecological niche) of the forest, grassland, shrubland, and desert under present and future climate conditions based on the natural climax vegetation distribution across China. We show that the potential forestation lands are mainly located in eastern China, which is east of the Hu Line (also known as the Heihe-Tengchong Line). Under future climate change, forests will shift substantially in the latitudinal, longitudinal, and elevational distribution. Potential forestation lands will increase by 33.1 million hectares through the 2070s, mainly due to the conversions of shrub and grassland to forests along the Hu Line. Our prediction map also indicates that grassland rehabilitation is the universal optimal vegetation restoration strategy in areas west of the Hu Line. This analysis is consistent with much of the observed evidence of forestation failures and recent climate-change-induced forest range shifts. Our results provide an overview and further show the importance of adaptive science-based forestation planning and forest management.

Modeling Eastern Russian High Arctic Geese (Anser fabalis, A. albifrons) during moult and brood rearing in the 'New Digital Arctic'.

Many polar species and habitats are now affected by man-made global climate change and underlying infrastructure. These anthropogenic forces have resulted in clear implications and many significant changes in the arctic, leading to the emergence of new climate, habitats and other issues including digital online infrastructure representing a 'New Artic'. Arctic grazers, like Eastern Russian migratory populations of Tundra Bean Goose Anser fabalis and Greater White-fronted Goose A. albifrons, are representative examples and they are affected along the entire flyway in East Asia, namely China, Japan and Korea. Here we present the best publicly-available long-term (24 years) digitized geographic information system (GIS) data for the breeding study area (East Yakutia and Chukotka) and its habitats with ISO-compliant metadata. Further, we used seven publicly available compiled Open Access GIS predictor layers to predict the distribution for these two species within the tundra habitats. Using BIG DATA we are able to improve on the ecological niche prediction inference for both species by focusing for the first time specifically on biological relevant population cohorts: post-breeding moulting non-breeders, as well as post-breeding parent birds with broods. To assure inference with certainty, we assessed it with 4 lines of evidence including alternative best-available open access field data from GBIF.org as well as occurrence data compiled from the literature. Despite incomplete data, we found a good model accuracy in support of our evidence for a robust inference of the species distributions. Our predictions indicate a strong publicly best-available relative index of occurrence (RIO). These results are based on the quantified ecological niche showing more realistic gradual occurrence patterns but which are not fully in agreement with the current strictly applied parsimonious flyway and species delineations. While our predictions are to be improved further, e.g. when synergetic data are made freely available, here we offer within data caveats the first open access model platform for fine-tuning and future predictions for this otherwise poorly represented region in times of a rapid changing industrialized 'New Arctic' with global repercussions.

High-resolution prediction of American red squirrel in Interior Alaska: a role model for conservation using open access data, machine learning, GIS and LIDAR.

American red squirrels (Tamiasciurus hudsonicus) are small mammals that are abundantly distributed throughout North America. Urbanization in the Anthropocene is now a global process, and squirrels live in affected landscapes. This leads to squirrels adjusting to human developments. Not much is known about the distribution of squirrels and squirrel middens near humans, especially not in the subarctic and sub-urbanized regions. Although this species is hunted, there are no real publicly available distribution and abundance estimates nor management plans and bag limits for squirrels in Alaska or in the United States known by us, except the endangered Mt. Graham squirrel. In general, insufficient squirrel conservation research is carried out; they are underrepresented in research and its literature. To further the science-based management for such species, this study aims to generate the first digital open access workflow as a generic research template for small mammal work including the latest machine learning of open source and high-resolution LIDAR data in an Open Source Geographic Information System (QGIS) and ArcGIS. Machine learning has proven to be less modeler biased and improve accuracy of the analysis outcome, therefore it is the preferred approach. This template is designed to be rapid, simple, robust, generic and effective for being used by a global audience. As a unique showcase, here a squirrel midden survey was carried out for two years (2016 and 2017). These squirrel middens were detected in a research area of 45,5 hectares (0,455 km2) in downtown Fairbanks, interior boreal forest of Alaska, U.S. Transect distances were geo-referenced with a GPS and adjusted to the visual conditions to count all squirrel middens within the survey area. Different layers of proximity to humans and habitat characteristics were assembled using aerial imagery and LIDAR data (3D data needed for an arboreal species like the red squirrels) consisting of a 3 × 3 m resolution. The layer data was used to train a predictive distribution model for red squirrel middens with machine learning. The model showed the relative index of occurrence (RIO) in a map and identified canopy height, distance to trails, canopy density and the distance to a lake, together, as the strongest predictors for squirrel midden distribution whereas open landscape and disturbed areas are avoided. It is concluded that squirrels select for high and dense forests for middens while avoiding human disturbance. This study is able to present a machine learning template to easily and rapidly produce an accurate abundance prediction which can be used for management implications.

The ecological niche of reported rabies cases in Canada is similar to Alaska.

The ecology of rabies in the circumpolar North is still not well understood. We use machine learning, a geographic information system and data explicit in time and space obtained for reported rabies cases and predictors in Canada to develop an ecological niche model for the distribution of reported rabies cases in the American north (Alaska and Canada). The ecological niche model based on reported rabies cases in Canada predicted reported rabies cases in Alaska, suggesting a rather robust inference and even similar drivers on a continental scale. As found in Alaska, proximity to human infrastructure-specifically along the coast-was a strong predictor in the detection of rabies cases in Canada. Also, this finding highlights the need for a more systematic landscape sampling for rabies infection model predictions to better understand and tackle the ecology of this important zoonotic disease on a landscape scale at some distance from human infrastructure in wilderness areas.

Palearctic passerine migrant declines in African wintering grounds in the Anthropocene (1970-1990 and near future): A conservation assessment using publicly available GIS predictors and machine learning.

The Anthropocene causes many massive and novel impacts, e.g., on migratory birds and their habitats. Many species of migratory birds have been declining on the Palearctic-African flyway in recent decades. To investigate possible impacts on a continental scale, we used 18 predictors extracted from 16 publicly available GIS layers in combination with machine learning methods on the sub-Saharan distributions of 64 passerine migrant species. These bird species were categorized as having experienced a 'Large Decline' (n = 12), a 'Moderate Decline' (n = 6) or 'No Decline' (n = 46) based on European census data from 1970 to 1990. Therefore, we present the first study for these species which uses publically available Open Access GIS-data and a multivariate (n = 18) and multi-species (n = 64) machine learning approach to deduce possible past impacts. We furthermore modelled likely future human population change and climate change impacts. We identified three predictor themes related to the distributions and declines of these migratory birds: (I) locations, represented by African ecosystems, countries, and soil types; (II) human population pressures and land-use intensities, the latter represented by land-use categories, habitat area, and cropland proportion; and (III) climatic predictors. This is the first study to relate migratory bird declines to human population pressures and land-use intensities using this type of analysis. We also identified areas of conservation concern, such as the Sahel region. Our models also predict that the declining trends of migratory birds will continue into the foreseeable future across much of Africa. We then briefly discuss some wider conservation implications in the light of the increasing drivers of biodiversity change associated with the Anthropocene as well as some possible solutions. We argue that only comprehensive systemic change can mitigate the impacts on the migratory birds and their habitats.

Data mining and model-predicting a global disease reservoir for low-pathogenic Avian Influenza (A) in the wider pacific rim using big data sets.

Avian Influenza (AI) is a complex but still poorly understood disease; specifically when it comes to reservoirs, co-infections, connectedness and wider landscape perspectives. Low pathogenic (Low-path LP) AI in chickens caused by less virulent strains of AI viruses (AIVs)-when compared with highly pathogenic AIVs (HPAIVs)-are not even well-described yet or known how they contribute to wider AI and immune system issues. Co-circulation of LPAIVs with HPAIVs suggests their interactions in their ecological aspects. Here we show for the Pacific Rim an international approach how to data mine and model-predict LP AI and its ecological niche with machine learning and open access data sets and geographic information systems (GIS) on a 5 km pixel size for best-possible inference. This is based on the best-available data on the issue (~ 40,827 records of lab-analyzed field data from Japan, Russia, Vietnam, Mongolia, Alaska and Influenza Research Database (IRD) and U.S. Department of Agriculture (USDA) database sets, as well as 19 GIS data layers). We sampled 157 hosts and 110 low-path AIVs with 32 species as drivers. The prevalence across low-path AIV subtypes is dominated by Muscovy ducks, Mallards, Whistling Swans and gulls also emphasizing industrial impacts for the human-dominated wildlife contact zone. This investigation sets a good precedent for the study of reservoirs, big data mining, predictions and subsequent outbreaks of HPAI and other pandemics.

The use of classification and regression algorithms using the random forests method with presence-only data to model species' distribution.

Random forests (RF) is a powerful species distribution model (SDM) algorithm. This ensemble model by default can produce categorical and numerical species distribution maps based on its classification tree (CT) and regression tree (RT) algorithms, respectively. The CT algorithm can also produce numerical predictions (class probability). Here, we present a detailed procedure involving the use of the CT and RT algorithms using the RF method with presence-only data to model the distribution of species. CT and RT are used to generate numerical prediction maps, and then numerical predictions are converted to binary predictions through objective threshold-setting methods. We also applied simple methods to deal with collinearity of predictor variables and spatial autocorrelation of species occurrence data. A geographically stratified sampling method was employed for generating pseudo-absences. The detailed procedural framework is meant to be a generic method to be applied to virtually any SDM prediction question using presence-only data. •How to use RF as a standard method for generic species distributions with presence-only data•How to choose RF (CT or RT) methods for the distribution modeling of species•A general and detailed procedure for any SDM prediction question.

Parapatric subspecies of Macaca assamensis show a marginal overlap in their predicted potential distribution: Some elaborations for modern conservation management.

Phylogenetic niche conservatism implies that sister taxa will have similar niches, although the niches of disjunct subspecies may evolve differently. This study uses Macaca assamensis, subspecies assamensis and pelops, to investigate the similarities of realized climatic niches of two disjunct subspecies (separated by the Brahmaputra River) along with a similarity analysis of their respective regions' climate. Modeled distributions were used to quantify their potential distribution under current and future climate scenarios. The climatic similarity between regions of each subspecies was tested with principal component analysis (PCA), and the realized climatic niche overlap between two subspecies was tested with a multivariate analysis of variance (MANOVA) on a subset of the least correlated variables out of 24 publicly available topo-bioclimatic variables. Tukey's honest significance difference (HSD) was used to test the range differences (on all 24 variables) between subspecies. The potential distribution of both taxa in the current climate and projected future climate was model-predicted using MaxEnt and Random Forest. We found significantly different climatic ranges for 21 predictors (HSD; p < 0.05) for the two subspecies, significantly different climatic conditions for their regions (using PCA; p < 0.001), and significantly different realized climatic niches for the two subspecies (MANOVA; p < 0.001). The distribution models generated a larger potential area than the currently known distributions. Although literature show that the Brahmaputra River is an effective dispersal barrier, we found some of the neighboring geographic range for both subspecies appears to be potentially suitable for the other taxon. The projected future potential areas indicate that some parts of the currently occupied geography, mostly southern parts, may become climatically unsuitable, whereas other new geographical areas may become suitable. Most of these new potential areas will be toward the north where higher and fragmented mountains, which has conservation implications.

Combining occurrence and abundance distribution models for the conservation of the Great Bustard.

Species distribution models (SDMs) have become important and essential tools in conservation and management. However, SDMs built with count data, referred to as species abundance models (SAMs), are still less commonly used to date, but increasingly receiving attention. Species occurrence and abundance do not frequently display similar patterns, and often they are not even well correlated. Therefore, only using information based on SDMs or SAMs leads to an insufficient or misleading conservation efforts. How to combine information from SDMs and SAMs and how to apply the combined information to achieve unified conservation remains a challenge. In this study, we introduce and propose a priority protection index (PI). The PI combines the prediction results of the occurrence and abundance models. As a case study, we used the best-available presence and count records for an endangered farmland species, the Great Bustard (Otis tarda dybowskii), in Bohai Bay, China. We then applied the Random Forest algorithm (Salford Systems Ltd. Implementation) with eleven predictor variables to forecast the spatial occurrence as well as the abundance distribution. The results show that the occurrence model had a decent performance (ROC: 0.77) and the abundance model had a RMSE of 26.54. It is noteworthy that environmental variables influenced bustard occurrence and abundance differently. The area of farmland, and the distance to residential areas were the top important variables influencing bustard occurrence. While the distance to national roads and to expressways were the most important influencing abundance. In addition, the occurrence and abundance models displayed different spatial distribution patterns. The regions with a high index of occurrence were concentrated in the south-central part of the study area; and the abundance distribution showed high populations occurrence in the central and northwestern parts of the study area. However, combining occurrence and abundance indices to produce a priority protection index (PI) to be used for conservation could guide the protection of the areas with high occurrence and high abundance (e.g., in Strategic Conservation Planning). Due to the widespread use of SDMs and the easy subsequent employment of SAMs, these findings have a wide relevance and applicability than just those only based on SDMs or SAMs. We promote and strongly encourage researchers to further test, apply and update the priority protection index (PI) elsewhere to explore the generality of these findings and methods that are now readily available.

Machine Learning Model Analysis of Breeding Habitats for the Black-necked Crane in Central Asian Uplands under Anthropogenic Pressures.

The black-necked crane (Grus nigricollis) is the only alpine crane species and is endemic to the Tibetan Plateau. The breeding habitats of this species are poorly understood, which greatly hampers practical research and conservation work. Using machine learning methods and the best-available data from our 7,000-kilometer mega-transect survey and open access data, we built the first species distribution model (SDM) to analyze the black-necked crane's breeding habitats. Our model showed that current conservation gaps account for 26.7% of its predicted breeding habitats. Specifically, the northern parts of the Hengduan Mountains and the southeastern Tibet Valley, the northern side of the middle Kunlun Mountains, parts of the Pamir Plateau, the northern Pakistan Highlands and the western Hindu Kush should be considered as its main potential breeding areas. Additionally, our model suggested that the crane prefers to breed in alpine meadows at an elevation over 2,800 m, a maximum temperature of the warmest month below 20.5 °C, and a temperature seasonality above 7,800 units. The identified conservation gaps and potential breeding areas can aid in clearly prioritizing future conservation and research, but more attention and study should be directed to the unassessed Western Development of China to secure this endangered crane lineage and other wildlife on the Tibetan Plateau.

Ecological niche modeling of rabies in the changing Arctic of Alaska.

BACKGROUND: Rabies is a disease of global significance including in the circumpolar Arctic. In Alaska enzootic rabies persist in northern and western coastal areas. Only sporadic cases have occurred in areas outside of the regions considered enzootic for the virus, such as the interior of the state and urbanized regions. RESULTS: Here we examine the distribution of diagnosed rabies cases in Alaska, explicit in space and time. We use a geographic information system (GIS), 20 environmental data layers and provide a quantitative non-parsimonious estimate of the predicted ecological niche, based on data mining, machine learning and open access data. We identify ecological correlates and possible drivers that determine the ecological niche of rabies virus in Alaska. More specifically, our models show that rabies cases are closely associated with human infrastructure, and reveal an ecological niche in remote northern wilderness areas. Furthermore a model utilizing climate modeling suggests a reduction of the current ecological niche for detection of rabies virus in Alaska, a state that is disproportionately affected by a changing climate. CONCLUSIONS: Our results may help to better inform public health decisions in the future and guide further studies on individual drivers of rabies distribution in the Arctic.

Why choose Random Forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence.

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.

Characterization of a genetic and antigenic variant of avian paramyxovirus 6 isolated from a migratory wild bird, the red-necked stint (Calidris ruficollis).

A hemagglutinating virus (8KS0813) was isolated from a red-necked stint. Hemagglutination inhibition and neutralization tests indicated that 8KS0813 was antigenically related to a prototype strain, APMV-6/duck/Hong Kong/18/199/77, but with an 8- and 16-fold difference, respectively, in their titers. The full genome sequence of 8KS0813 showed 98.6 % nucleotide sequence identity to that of APMV-6/duck/Italy/4524-2/07, which has been reported to belong to an APMV-6 subgroup, and showed less similarity to that of the prototype strain (70.6 % similarity). The growth of 8KS0813 and the prototype strain in four different cell cultures was greatly enhanced by adding trypsin. Interestingly, this virus induced syncytia only in Vero cells. 8KS0813 was identified as APMV-6/red-necked stint/Japan/8KS0813/08, but it is antigenically and genetically distinguishable from the prototype strain, suggesting that variant APMV-6 is circulating in migratory birds.