Where the bears roam in Majella National Park, Italy

Global transboundary transmission path and risk of Mpox revealed with Least Cost Path model

OBJECTIVES

The recent surge of Mpox outbreaks in multiple countries has garnered global attention. As of July 12, 2023, there have been 88,288 reported cases of Mpox worldwide. Although genetic variation was not found to be the cause of the epidemic outbreak, the reasons for its rapid spread remain unclear.

METHODS

Using the niche method, this study identified high-risk regions for Mpox and determined that human factors are the primary contributors to global risks. To further investigate, a travel network resistance surface was created based on various modes of transportation and was combined with sea, airline, highway, and railway routes to construct the least cost path for human travel networks in different risk areas.

RESULTS

The results indicated that high-risk regions for Mpox are mainly concentrated in Europe and the United States, with large risk ranges and high-risk values. The least cost path revealed three primary transmission paths rely on developed transportation networks, including internal transmission in North America, Europe-Africa, and Europe-Asia-Africa. These findings suggest that human activities, facilitated by developed travel networks, remain the main contributing factor to the spread.

CONCLUSIONS

In summary, based on the Mpox epidemic report, this study conducted risk prediction and driving factor analysis on Mpox. The research results indicate that human use of transportation for long-distance activities is a key factor leading to the rapid spread of the virus. Subsequently, we focused on studying the global transmission pathways of Mpox and revealed several transmission pathways with high global population migration rates by constructing the LCPs between different high-risk areas. This study also emphasizes the importance of applying early monitoring data of Mpox to model risk prediction in controlling emerging infectious diseases, providing a new perspective for controlling Mpox and similar diseases.

Past and current climate as a driver in shaping the distribution of the Longitarsus candidulus species group (Coleoptera: Chrysomelidae)

Longitarsus candidulus (Foudras) is a thermophilic flea beetle species widely distributed in the Mediterranean Basin and associated with Daphne gnidium L. and Thymelaea hirsuta (L.). Longitarsus laureolae Biondi and Longitarsus leonardii Doguet, phylogenetically closely related to L. candidulus, show together a peculiar and rare disjunct distribution along the central-southern Apennines and the Cantabrian-Pyrenean mountain system, respectively. Both are associated with Daphne laureola L. in mesophilic habitats. We used "ecological niche modeling" to infer the Pleistocene dynamics in the distribution of the three flea beetle species and their host plants. We interpreted their current distributions, paying particular attention to the presumed time of species divergence as inferred from recent studies. The differentiation of L. laureolae and L. leonardii from L. candidulus likely represents a response to the marked climatic changes during the Late Pliocene. Such a split was likely associated with a trophic niche shift of the laureolae/leonardii ancestor towards the typically mesophilic host plant D. laureola. The subsequent split between L. laureolae and L. leonardii, possibly due at first to the niche competition, was then boosted by an allopatric divergence during the Middle Pleistocene, likely caused by a large area of low environmental suitability for both species, mainly located between the northern Apennines and the south-western Alps.

Unveiling the spatial distribution and transboundary pathways of FMD serotype O in Western China and its bordering countries

Foot-and-mouth disease (FMD) is a severe, highly contagious viral disease of livestock that has a significant economic impact on domestic animals and threatens wildlife survival in China and border countries. However, effective surveillance and prevention of this disease is often incomplete and unattainable due to the cost, the great diversity of wildlife hosts, the changing range and dynamics, and the diversity of FMDV. In this study, we used predictive models to reveal the spread and risk of FMD in anticipation of identifying key nodes to control its spread. For the first time, the spatial distribution of FMD serotype O was predicted in western China and border countries using a niche model, which is a combination of eco-geographic, human, topographic, and vegetation variables. The transboundary least-cost pathways (LCPs) model for ungulates in the study area were also calculated. Our study indicates that FMD serotype O survival is seasonal at low altitudes (March and June) and more sensitive to temperature differences at high altitudes. FMD serotype O risk was higher in Central Asian countries and both were highly correlated with the population variables. Ten LCPs were obtained representing Pakistan, Kazakhstan, Kyrgyzstan, and China.

Mapping high probability area for the Bacillus anthracis occurrence in wildlife protected area, South Omo, Ethiopia

Anthrax is a zoonotic disease caused by a spore-forming gram-positive bacterium, Bacillus anthracis. Increased anthropogenic factors inside wildlife-protected areas may worsen the spillover of the disease at the interface. Consequently, environmental suitability prediction for B. anthracis spore survival to locate a high-risk area is urgent. Here, we identified a potentially suitable habitat and a high-risk area for appropriate control measures. Our result revealed that a relatively largest segment of Omo National Park, about 23.7% (1,218 square kilometers) of the total area; 36.6% (711 square kilometers) of Mago National Park, and 29.4% (489 square kilometers) of Tama wildlife Reserve predicted as a high-risk area for the anthrax occurrence in the current situation. Therefore, the findings of this study provide the priority area to focus on and allocate resources for effective surveillance, prevention, and control of anthrax before it causes devastating effects on wildlife.

Assessing eco-geographic influences on COVID-19 transmission: a global analysis

COVID-19 has been massively transmitted for almost 3 years, and its multiple variants have caused serious health problems and an economic crisis. Our goal was to identify the influencing factors that reduce the threshold of disease transmission and to analyze the epidemiological patterns of COVID-19. This study served as an early assessment of the epidemiological characteristics of COVID-19 using the MaxEnt species distribution algorithm using the maximum entropy model. The transmission of COVID-19 was evaluated based on human factors and environmental variables, including climate, terrain and vegetation, along with COVID-19 daily confirmed case location data. The results of the SDM model indicate that population density was the major factor influencing the spread of COVID-19. Altitude, land cover and climatic factor showed low impact. We identified a set of practical, high-resolution, multi-factor-based maximum entropy ecological niche risk prediction systems to assess the transmission risk of the COVID-19 epidemic globally. This study provided a comprehensive analysis of various factors influencing the transmission of COVID-19, incorporating both human and environmental variables. These findings emphasize the role of different types of influencing variables in disease transmission, which could have implications for global health regulations and preparedness strategies for future outbreaks.

Spatial risk of Haemaphysalis longicornis borne Dabieshan tick virus (DBTV) in China

The uncertainty and unknowability of emerging infectious diseases have caused many major public health and security incidents in recent years. As a new tick-borne disease, Dabieshan tick virus (DBTV) necessitate systematic epidemiological and spatial distribution analysis. In this study, tick samples from Liaoning Province were collected and used to evaluate distribution of DBTV in ticks. Outbreak points of DBTV and the records of the vector Haemaphysalis longicornis in China were collected and used to establish a prediction model using niche model combined with environmental factors. We found that H. longicornis and DBTV were widely distributed in Liaoning Province. The risk analysis results showed that the DBTV in the eastern provinces of China has a high risk, and the risk is greatly influenced by elevation, land cover, and meteorological factors. The risk geographical area predicted by the model is significantly larger than the detected positive areas, indicating that the etiological survey is seriously insufficient. This study provided molecular and important epidemiological evidence for etiological ecology of DBTV. The predicted high-risk areas indicated the insufficient monitoring and risk evaluation and the necessity of future monitoring and control work.

Estimating the spatial distribution of African swine fever outbreak in China by combining four regional-level spatial models

The outbreaks of African Swine Fever (ASF) in China are ongoing, and the inadequate management of the pig supply chain is criticized. In the past four years, a series of preventive and control measures have been supplied national wide, while the outbreaks have not been terminated. This suggests the existing animal disease management at the district level may not be appropriate to control ASF under the current situation of the ASF outbreak in China. It is urgent to further describe real distribution areas of ASF in China. In this study, we combined four regional-scale models to predict the risk distribution of ASF in mainland China and identify risk factors related to ASF outbreaks. The results showed that the four regional-scale models were more accurate in predicting the ASF outbreaks than the nationwide scale model. The four regional-scale models identified the potential risk factors associated with ASF outbreaks, such as population density, pig density, land cover, temperature, and elevation factors. Moreover, seven clusters with high potential risk of ASF outbreaks were identified. Then, based on the results, we proposed more suitable prevention and control plans for ASF, which can assist the implementation of transport management policies within and between risk clusters.

Driving effect of multiplex factors on Mpox in global high-risk region, implication for Mpox based on one health concept

Mpox is an ongoing viral zoonotic disease epidemic worldwide. Being different from conventional animal-to-human transmission, the present outbreak is mainly caused by human-to-human transmission of Mpox virus, putting forward the risk of worldwide epidemic. The current spatial distribution characteristics and risk area prediction are urgently needed for preparedness for prevention and control of the disease based on the One Health strategy. In the present study, the global outbreak point of Mpox virus were collected and used to predict potential global risk of Mpox virus with ecological niche model constructed with a combination of eco-geographical, anthropoid, meteorological, and host variables. The results showed that human factors are the key to the risk and prevalence of Mpox. The risk map indicated that Mpox may affect extensive areas worldwide. Europe and North America have the highest risk of Mpox. Although most areas have never recorded Mpox before, there are some high-risk areas in Asia. Our findings highlight population density is the most important contributing factor for high-risk area. Many large cities with dense populations, developed transportation, and high migration rate in the world, are in high risks. At present, the spread of Mpox is highly valued in the world and strict prevention and control measures have been taken. However, under the influence of human factors, Mpox has the potential of a global pandemic. The risk area prediction and main risk factors provide key information for targeted preparedness for prevention and control of Mpox outbreak and avoiding potential global epidemic through the One Health approach.

Risk transboundary transmission areas and driving factors of brucellosis along the borders between China and Mongolia

OBJECTIVE

Brucellosis is a common and neglected zoonotic infectious disease worldwide caused by Brucella. However, transboundary transmissions among countries, particularly those with high incidences, are seldom investigated. In the present study, by taking China and Mongolia as examples, we aim to identify transboundary transmission risk and driving factors of brucellosis along borders.

METHODS

167 brucellosis outbreak locations along the border between China and Mongolia were collected. Wildlife distribution and cross-border activities were mapped. Maximum entropy approach modeling was conducted to predict the potential risk of prevalence of brucellosis with meteorological factors, geographical environment, economic development, living habits et al. The accuracy of the models was assessed by the area under the receiver operating characteristic (ROC) curve (AUC), Kappa test, and correctly classified instances (CCI).

RESULTS

The spatial model performed excellent predictive performance with the predictor variables of soils, pastures, goat density, mean precipitation of the wettest month, temperature seasonality, and population density, which with the contribution and permutation important in 27.2 %, 31.9; 23.3 %, 6.8; 18.0 %, 17.2; 11.2 %, 18.1; 10. 3 %, 15.2; 10.0 %, 10.8. The calculated AUC, SD, Kappa, and CCI are 0.870, 0.001, 0.882, and 0.883, respectively. The distribution map of brucellosis showed high-risk areas along the borders.

CONCLUSIONS

Our study identified high-risk areas and the driving effect of brucellosis along the borders between China and Mongolia. Moreover, there is the possibility of cross-border wildlife activities in high-risk areas, which increases the risk of cross-border brucellosis transmission. The funding provides clues for cooperative prevention and control of brucellosis by reducing transboundary transmission.

Modeling the environmental suitability for Bacillus anthracis in the Qinghai Lake Basin, China

Bacillus anthracis is a gram-positive, rod-shaped and endospore-forming bacterium that causes anthrax, a deadly disease to livestock and, occasionally, to humans. The spores are extremely hardy and may remain viable for many years in soil. Previous studies have identified East Qinghai and neighbouring Gansu in northwest China as a potential source of anthrax infection. This study was carried out to identify conditions and areas in the Qinghai Lake basin that are environmentally suitable for B. anthracis distribution. Anthrax occurrence data from 2005-2016 and environmental variables were spatially modeled by a maximum entropy algorithm to evaluate the contribution of the variables to the distribution of B. anthracis. Principal Component Analysis and Variance Inflation Analysis were adopted to limit the number of environmental variables and minimize multicollinearity. Model performance was evaluated using AUC (area under the curve) ROC (receiver operating characteristics) curves. The three variables that contributed most to the suitability model for B. anthracis are a relatively high annual mean temperature of -2 to 0°C, (53%), soil type classified as; cambisols and kastanozems (35%), and a high human population density of 40 individuals per km2 (12%). The resulting distribution map identifies the permanently inhabited rim of the Qinghai Lake as highly suitable for B. anthracis. Our environmental suitability map and the identified variables provide the nature reserve managers and animal health authorities readily available information to devise both surveillance strategy and control strategy (administration of vaccine to livestock) in B. anthracis suitable regions to abate future epidemics.

Seasonality and Ecological Suitability Modelling for Anthrax (Bacillus anthracis) in Western Africa

Simple Summary

Anthrax is a globally distributed, neglected, underreported, soil-borne zoonotic disease. In West Africa, the disease is hyper-endemic, severely affecting the livestock sector. Many challenges exist to control the disease in this region, particularly constraints on financial and human resources. Therefore, methods that can be utilized to improve reporting, guide and prioritize surveillance and control activities and rationalize the allocation of limited resources are crucial. In this study, we showed how to optimize the use of fragmented, heterogeneous and limited precise reporting data of anthrax in Burkina Faso, Ghana, Togo, Benin and Niger to understand risk periods as well as identify and predict risk areas. To achieve this, we used anthrax data from different databases in combination with environmental and climate variables and geospatial remote sensing techniques. Our study demonstrated that the number of anthrax outbreaks by month increase with the increasing monthly rates of change in precipitation and normalized difference vegetation index (NDVI) during the transition period from the dry to the wet season. Livestock density, precipitation, NDVI and alkaline soils were the main predictors of anthrax suitability in the region. Our findings on anthrax seasonality and ecological suitability can inform surveillance, prevention and control programs undertaken by animal and public health authorities and enhance collaborative One Health strategies.

Abstract

Anthrax is hyper-endemic in West Africa affecting wildlife, livestock and humans. Prediction is difficult due to the lack of accurate outbreak data. However, predicting the risk of infection is important for public health, wildlife conservation and livestock economies. In this study, the seasonality of anthrax outbreaks in West Africa was investigated using climate time series and ecological niche modeling to identify environmental factors related to anthrax occurrence, develop geospatial risk maps and identify seasonal patterns. Outbreak data in livestock, wildlife and humans between 2010 and 2018 were compiled from different sources and analyzed against monthly rates of change in precipitation, normalized difference vegetation index (NDVI) and land surface temperature. Maximum Entropy was used to predict and map the environmental suitability of anthrax occurrence. The findings showed that: (i) Anthrax outbreaks significantly (99%) increased with incremental changes in monthly precipitation and vegetation growth and decremental changes in monthly temperature during January–June. This explains the occurrence of the anthrax peak during the early wet season in West Africa. (ii) Livestock density, precipitation seasonality, NDVI and alkaline soils were the main predictors of anthrax suitability. (iii) Our approach optimized the use of limited and heterogeneous datasets and ecological niche modeling, demonstrating the value of integrated disease notification data and outbreak reports to generate risk maps. Our findings can inform public, animal and environmental health and enhance national and regional One Health disease control strategies.

Predicting the possibility of African horse sickness (AHS) introduction into China using spatial risk analysis and habitat connectivity of Culicoides

African horse sickness (AHS) is a devastating equine infectious disease. On 17 March 2020, it first appeared in Thailand and threatened all the South-East Asia equine industry security. Therefore, it is imperative to carry out risk warnings of the AHS in China. The maximum entropy algorithm was used to model AHS and Culicoides separately by using climate and non-climate variables. The least cost path (LCP) method was used to analyze the habitat connectivity of Culicoides with the reclassified land cover and altitude as cost factors. The models showed the mean area under the curve as 0.918 and 0.964 for AHS and Culicoides. The prediction result map shows that there is a high risk area in the southern part of China while the habitats of the Culicoides are connected to each other. Therefore, the risk of introducing AHS into China is high and control of the border area should be strengthened immediately.

Transboundary spread of peste des petits ruminants virus in western China: A prediction model

In pan Pamir Plateau countries, Peste des petits ruminants (PPR) has brought huge losses to the livestock industry and threaten the endangered wildlife. In unknown regions, revealing PPRV transmission among countries is the premise of effective prevention and control, therefore calls for quantified monitoring on disease communication among countries. In this paper, a MaxEnt model was built for the first time to predict the PPR risk within the research area. The least cost path (LCP) for PPR transboundary communication were calculated and referred to as the maximum available paths (MAP). The results show that there are many places with high-risk in the research area, and the domestic risk in China is lower than that in foreign countries and is mainly determined by human activities. Five LCPs representing corridors among Kazakhstan, Tajikistan, Pakistan, India and China were obtained. This study proves for the first time that there is the possibility of cross-border transmission of diseases by wild and domestic animals. In the future, it will play an important role in monitoring the PPR epidemic and blocking-up its cross-border transmission.

A predictive analysis on the risk of peste des petits ruminants in livestock in the Trans-Himalayan region and validation of its transboundary transmission paths

Although the Trans-Himalayan region (THR) is an important endemic and rendezvous area of peste des petits ruminants (PPR), monitoring and prevention measurements are difficult to execute because of the rough geographical conditions. Besides, a heterogeneous breeding system and the poor veterinary service of susceptible animals compound the existing problems. Here, we propose a forecasting system to define the key points of PPR prevention and aid the countries in saving time, labor, and products to achieve the goal of the global eradication project of PPR. The spatial distribution of PPR was predicted in the THR for the first time using a niche model that was constructed with a combination of eco-geographical, anthropoid, meteorological, and host variables. The transboundary least-cost paths (LCPs) of small ruminants in the THR were also calculated. Our results reveal that the low-elevation area of the THR had a higher PPR risk and was mainly dominated by human variables. The high-elevation area had lower risk and was mainly dominated by natural variables. Eight LCPs representing corridors among India, Nepal, Bhutan, Bangladesh, and China were obtained. This confirmed the potential risk of transboundary communication by relying on PPR contamination on the grasslands for the first time. The predicted potential risk communication between the two livestock systems and landscapes (high and low elevation) might play a role in driving PPR transboundary transmission.

Could wild boar be the Trans-Siberian transmitter of African swine fever?

China has experienced a sudden multi-focal and multi-round of African swine fever (ASF) outbreaks during 2018. The subsequent epidemiological survey resulted in a debate including the possibility of a transboundary spread from European Russia to China through wild boar. We contribute to the debate by assessing a hypothetical overland Euro-Siberian transmission path and its associated ASF arrival dates. We selected the maximum entropy algorithm for spatial modelling of ASF-infected wild boar and the Spatial Distribution Modeller in ArcGIS to plot Least Cost Paths (LCPs) between Eastern Europe and NE China. The arrival dates of ASF-infected wild boar have been predicted by cumulative maximum transmission distances per season and cover with their associated minimum time intervals along the LCPs. Our results show high costs for wild boar to cross Kazakhstan, Xinjiang (NW China) and/or Mongolia to reach NE China. Instead, the Paths lead almost straight eastward along the 59.5° northern latitude through Siberia and would have taken a minimum of 219 or 260 days. Therefore, infected wild boar moving all the way along the LCP could not have been the source of the ASF infection in NE China on 2 August 2018.

The potential distribution and dynamics of important vectors Culex pipiens pallens and Culex pipiens quinquefasciatus in China under climate change scenarios: an ecological niche modelling approach

BACKGROUND

Intense studies have been carried out on the effects of climate change on vector-borne diseases and vectors. Culex pipiens pallens and Culex pipiens quinquefasciatus are two medically concerned mosquito species in temperate and tropical areas, which serve as important disease-transmitting pests of a variety of diseases. The ongoing geographical expansion of these mosquitoes has brought an increasing threat to public health.

RESULTS

Based on mosquito occurrence records and high-resolution environmental layers, an ecological niche model was established to model their current and future potential distribution in China. Our model showed that the current suitable area for Cx. p. pallens is distributed in the central, eastern and northern parts of China, while Cx. p. quinquefasciatus is distributed in vast areas in southern China. Under future climate change scenarios, both species are predicted to expand their range to varying degrees and RCP 8.5 provides the largest expansion. Northward core shifts will occur in ranges of both species. Environmental variables which have significant impact on the distribution of mosquitoes were also revealed by our model.

CONCLUSION

Severe habitat expansion of vectors is likely to occur in the future 21st century. Our models mapped the high-risk areas and risk factors which needs to be paid attention. The results of our study can be referenced in further ecological surveys and will guide the development of strategies for the prevention and control of vector-borne diseases. © 2020 Society of Chemical Industry.

Modelling Current and Future Potential Habitats for Plantations of Eucalyptus grandis Hill ex Maiden and E. dunnii Maiden in Uruguay

Eucalyptus grandis and E. dunnii have high productive potential in the South of Brazil, Uruguay, and central Argentina. This is based on the similarity of the climate and soil of these areas, which form an eco-region called Campos. However, previous results show that these species have differences in their distribution caused by the prioritization of Uruguayan soils for forestry, explained by the particular conditions of each site. In this study, the site variables (climate, soil, and topography) that better explain the distribution of both species were identified, and prediction models of current and future distribution were adjusted for different climate change scenarios (years 2050 and 2070). The distribution of E. grandis was associated with soil parameters, whereas for E. dunnii a greater effect of the climatic variables was observed. The ensemble biomod2 model was the most precise with regard to predicting the habitat for both species with respect to the simple models evaluated. For E. dunnii, the average values of the AUC, Kappa, and TSS index were 0.98, 0.88, and 0.77, respectively. For E. grandis, their values were 0.97, 0.86, and 0.80, respectively. In the projections of climatic change, the distribution of E. grandis occurrence remains practically unchanged, even in the scenarios of temperature increase. However, current distribution of E. dunnii shows high susceptibility in a scenario of increased temperature, to the point that most of the area currently planted may be at risk. Our results might be useful to political government and foresters for decision making in terms of future planted areas.

Projected impacts of climate change on the range and phenology of three culturally-important shrub species

Climate change is shifting both the habitat suitability and the timing of critical biological events, such as flowering and fruiting, for plant species across the globe. Here, we ask how both the distribution and phenology of three food-producing shrubs native to northwestern North America might shift as the climate changes. To address this question, we compared gridded climate data with species location data to identify climate variables that best predicted the current bioclimatic niches of beaked hazelnut (Corylus cornuta), Oregon grape (Mahonia aquifolium), and salal (Gaultheria shallon). We also developed thermal-sum models for the timing of flowering and fruit ripening for these species. We then used multi-model ensemble future climate projections to estimate how species range and phenology may change under future conditions. Modelling efforts showed extreme minimum temperature, climate moisture deficit, and mean summer precipitation were predictive of climatic suitability across all three species. Future bioclimatic niche models project substantial reductions in habitat suitability across the lower elevation and southern portions of the species’ current ranges by the end of the 21^st century. Thermal-sum phenology models for these species indicate that flowering and the ripening of fruits and nuts will advance an average of 25 days by the mid-21^st century, and 36 days by the late-21^st century under a high emissions scenario (RCP 8.5). Future changes in the climatic niche and phenology of these important food-producing species may alter trophic relationships, with cascading impacts on regional ecosystems.

Investigating the Current and Future Co-Occurrence of Ambrosia artemisiifolia and Ophraella communa in Europe through Ecological Modelling and Remote Sensing Data Analysis

The common ragweed Ambrosia artemisiifolia has spread throughout Europe since the 1800s, infesting croplands and causing severe allergic reactions. Recently, the ragweed leaf beetle Ophraella communa was found in Italy and Switzerland; considering that it feeds primarily on A. artemisiifolia in its invaded ranges, some projects started biological control of this invasive plant through the adventive beetle. In this context of a ‘double’ invasion, we assessed the influence of climate change on the spread of these alien species through ecological niche modelling. Considering that A. artemisiifolia mainly lives in agricultural and urbanized areas, we refined the models using satellite remote-sensing data; we also assessed the co-occurrence of the two species in these patches. A. artemisiifolia is predicted to expand more than O. communa in the future, with the medium and high classes of suitability of the former increasing more than the latter, resulting in lower efficacy for O. communa to potentially control A. artemisiifolia in agricultural and urbanized patches. Although a future assessment was performed through the 2018 land-cover data, the predictions we propose are intended to be a starting point for future assessments, considering that the possibility of a shrinkage of target patches is unlikely to occur.

High probability areas for ASF infection in China along the Russian and Korean borders

African swine fever (ASF) is a transcontinental, contagious, fatal virus disease of pig with devastating socioeconomic impacts. Interaction between infected wild boar and domestic pig may spread the virus. The disease is spreading fast from the west of Eurasia towards ASF-free China. Consequently, prediction of the distribution of ASF along the Sino-Russian-Korean borders is urgent. Our area of interest is Northeast China. The reported ASF-locations in 11 contiguous countries from the Baltic to the Russian Federation were extracted from the archive of the World Organization for Animal Health from July 19, 2007 to March 27, 2017. The locational records of the wild boar were obtained from literature. The environmental predictor variables were downloaded from the WorldClim website. Spatial rarefication and pair-wise geographic distance comparison were applied to minimize spatial autocorrelation of presence points. Principal component analysis (PCA) was used to minimize multi-collinearity among predictor variables. We selected the maximum entropy algorithm for spatial modelling of ASF and wild boar separately, combined the wild boar prediction with the domestic pig census in a single map of suids and overlaid the ASF with the suids map. The accuracy of the models was assessed by the AUC. PCA delivered five components accounting for 95.7% of the variance. Spatial autocorrelation was shown to be insignificant for both ASF and wild boar records. The spatial models showed high mean AUC (0.92 and 0.97) combined with low standard deviations (0.003 and 0.006) for ASF and wild boar, respectively. The overlay of the ASF and suids maps suggests that a relatively short sector of the Sino-Russian border has a high probability entry point of ASF at current conditions. Two sectors of the Sino-Korean border present an elevated risk.

Ensemble approach for potential habitat mapping of invasive Prosopis spp. in Turkana, Kenya

AIM

Prosopis spp. are an invasive alien plant species native to the Americas and well adapted to thrive in arid environments. In Kenya, several remote-sensing studies conclude that the genus is well established throughout the country and is rapidly invading new areas. This research aims to model the potential habitat of Prosopis spp. by using an ensemble model consisting of four species distribution models. Furthermore, environmental and expert knowledge-based variables are assessed.

LOCATION

Turkana County, Kenya.

METHODS

We collected and assessed a large number of environmental and expert knowledge-based variables through variable correlation, collinearity, and bias tests. The variables were used for an ensemble model consisting of four species distribution models: (a) logistic regression, (b) maximum entropy, (c) random forest, and (d) Bayesian networks. The models were evaluated through a block cross-validation providing statistical measures.

RESULTS

The best predictors for Prosopis spp. habitat are distance from water and built-up areas, soil type, elevation, lithology, and temperature seasonality. All species distribution models achieved high accuracies while the ensemble model achieved the highest scores. Highly and moderately suitable Prosopis spp. habitat covers 6% and 9% of the study area, respectively.

MAIN CONCLUSIONS

Both ensemble and individual models predict a high risk of continued invasion, confirming local observations and conceptions. Findings are valuable to stakeholders for managing invaded area, protecting areas at risk, and to raise awareness.

Resolving distribution and population fragmentation in two leaf-tailed gecko species of north-east Australia: key steps in the conservation of microendemic species

North Queensland harbours many microendemic species. These species are of conservation concern due to their small and fragmented populations, coupled with threats such as fire and climate change. We aimed to resolve the distribution and population genetic structure in two localised Phyllurus leaf-tailed geckos: P. gulbaru and P. amnicola. We conducted field surveys to better resolve distributions, used Species Distribution Models (SDMs) to assess the potential distribution, and then used the SDMs to target further surveys. We also sequenced all populations for a mitochondrial gene to assess population genetic structure. Our surveys found additional small, isolated populations of both species, including significant range extensions. SDMs revealed the climatic and non-climatic variables that best predict the distribution of these species. Targeted surveys based on the SDMs found P. gulbaru at an additional two sites but failed to find either species at other sites, suggesting that we have broadly resolved their distributions. Genetic analysis revealed population genetic structuring in both species, including deeply divergent mitochondrial lineages. Current and potential threats are overlain on these results to determine conservation listings and identify management actions. More broadly, this study highlights how targeted surveys, SDMs, and genetic data can rapidly increase our knowledge of microendemic species, and direct management.

Demographic mechanisms underpinning genetic assimilation of remnant groups of a large carnivore

Current range expansions of large terrestrial carnivores are occurring following human-induced range contraction. Contractions are often incomplete, leaving small remnant groups in refugia throughout the former range. Little is known about the underlying ecological and evolutionary processes that influence how remnant groups are affected during range expansion. We used data from a spatially explicit, long-term genetic sampling effort of grizzly bears (Ursus arctos) in the Northern Continental Divide Ecosystem (NCDE), USA, to identify the demographic processes underlying spatial and temporal patterns of genetic diversity. We conducted parentage analysis to evaluate how reproductive success and dispersal contribute to spatio-temporal patterns of genetic diversity in remnant groups of grizzly bears existing in the southwestern (SW), southeastern (SE) and east-central (EC) regions of the NCDE. A few reproductively dominant individuals and local inbreeding caused low genetic diversity in peripheral regions that may have persisted for multiple generations before eroding rapidly (approx. one generation) during population expansion. Our results highlight that individual-level genetic and reproductive dynamics play critical roles during genetic assimilation, and show that spatial patterns of genetic diversity on the leading edge of an expansion may result from historical demographic patterns that are highly ephemeral.