From Nucleotides to Satellite Imagery: Approaches to Identify and Manage the Invasive Pathogen Xylella fastidiosa and Its Insect Vectors in Europe

Occurrence and potential diffusion of pine wilt disease mediated by insect vectors in China under climate change

BACKGROUND

Pine wilt disease (PWD), a major international quarantined forest pest, causes serious ecological and economic damage to Pinus species in Asia and Europe. In China, PWD has spread northeasterly and northwesterly beyond its original northern limits. Consequently, an evaluation of the insect vector-mediated occurrence and potential diffusion of PWD is needed to identify important transmission routes and control the spread of disease.

RESULTS

An optimized MaxEnt model was used to assess the current and future geographical distribution of Bursaphelenchus xylophilus and its insect vectors in China. The predicted suitable area for B. xylophilus colonization is currently 212.32 × 104 km2 and mainly concentrated in Central, East, Southwest and South China, although is anticipated to include the northwestern regions of China in the future. As for the insect vectors, Monochamus alternatus and M. saltuarius are expected to spread toward the northwest and southwest, respectively. The maximum predicted dispersion area of PWD mediated by M. alternatus, M. saltuarius and both species was 91.85 × 104, 218.76 × 104 and 29.99 × 104 km2, respectively, with potential diffusion areas being anticipated to increase in the future. Both the suitable probabilities and areas of B. xylophilus and its insect vectors were found to vary substantially along the latitudinal gradient, with the latitudinal range of these species being predicted to expand in the future.

CONCLUSION

This is the first study to investigate the potential diffusion areas of PWD mediated by insect vectors in China, and our finding will provide a vital theoretical reference and empirical basis for developing more effective management strategies for the control of PWD in China. © 2024 Society of Chemical Industry.

Modeling climate-related global risk maps of rice bacterial blight caused by Xanthomonas oryzae (Ishiyama 1922) using geographical information system (GIS)

Rice is a critical staple crop that feeds more than half of the world's population. Still, its production confronts various biotic risks, notably the severe bacterial blight disease produced by Xanthomonas oryzae. Understanding the possible effects of climate change on the geographic distribution of this virus is critical to ensuring food security. This work used ecological niche modeling and the Maxent algorithm to create future risk maps for the range of X. oryzae under several climate change scenarios between 2050 and 2070. The model was trained using 93 occurrence records of X. oryzae and five critical bioclimatic variables. It has an excellent predictive performance, with an AUC of 0.889. The results show that X. oryzae's potential geographic range and habitat suitability are expected to increase significantly under low (RCP2.6) and high (RCP8.5) emission scenarios. Key climatic drivers allowing this development include increased yearly precipitation, precipitation during the wettest quarter, and the wettest quarter's mean temperature. These findings are consistent with broader research revealing that climate change is allowing many plant diseases and other dangerous microbes to spread across the globe. Integrating these spatial predictions with data on host susceptibility, agricultural practices, and socioeconomic vulnerabilities can help to improve targeted surveillance, preventative, and management methods for reducing the growing threat of bacterial blight to rice production. Proactive, multidisciplinary efforts to manage the changing disease dynamics caused by climate change will be critical to assuring global food security in the future decades.

Impacts of Climate Change on the Habitat Suitability and Natural Product Accumulation of the Medicinal Plant Sophora alopecuroides L. Based on the MaxEnt Model

Sophora alopecuroides L., a perennial herb in the arid and semi-arid regions of northwest China, has the ecological functions of windbreaking and sand fixation and high medicinal value. In recent years, global warming and human activities have led to changes in suitable habitats for S. alopecuroides, which may affect the accumulation of natural products. In this study, MaxEnt 3.4 and ArcGIS 10.4 software were used to predict the distribution of potentially suitable habitats for S. alopecuroides in China under climate change. Furthermore, the geographical distribution of S. alopecuroides as affected by human activities, the differences in the content of natural products of S. alopecuroides between different suitable habitats, and the correlation between natural products and environmental factors were analyzed. The results showed that suitable habitats for S. alopecuroides were projected to expand in the future, and the major environmental factors were temperature (Bio1), rainfall (Bio18), and soil pH (pH). When Bio1, Bio18, and pH were 8.4283 °C, 7.1968 mm, and 9.9331, respectively, the distribution probability (P) of S. alopecuroides was the highest. After adding a human activity factor, the accuracy of the model prediction results was improved, and the area of suitable habitats was greatly reduced, showing a fragmented pattern. Meanwhile, habitat suitability had a specific effect on the content of natural products in S. alopecuroides. Specifically, the content of natural products in S. alopecuroides in wild habitats was higher than that in artificial cultivation, and highly suitable habitats showed higher contents than those in non-highly suitable habitats. The contents of total alkaloids and total flavonoids were positively correlated with human activities and negatively correlated with land use types. Among them, total alkaloids were negatively correlated with aspect, and total flavonoids were positively correlated with aspect. In addition, it is suggested that Xinjiang should be the priority planting area for S. alopecuroides in China, and priority should be given to protection measures in the Alashan area. Overall, this study provides an important foundation for the determination of priority planting areas and resource protection for S. alopecuroides.

Ten Challenges to Understanding and Managing the Insect-Transmitted, Xylem-Limited Bacterial Pathogen Xylella fastidiosa

An unprecedented plant health emergency in olives has been registered over the last decade in Italy, arguably more severe than what occurred repeatedly in grapes in the United States in the last 140 years. These emergencies are epidemics caused by a stealthy pathogen, the xylem-limited, insect-transmitted bacterium Xylella fastidiosa. Although these epidemics spurred research that answered many questions about the biology and management of this pathogen, many gaps in knowledge remain. For this review, we set out to represent both the U.S. and European perspectives on the most pressing challenges that need to be addressed. These are presented in 10 sections that we hope will stimulate discussion and interdisciplinary research. We reviewed intrinsic problems that arise from the fastidious growth of X. fastidiosa, the lack of specificity for insect transmission, and the economic and social importance of perennial mature woody plant hosts. Epidemiological models and predictions of pathogen establishment and disease expansion, vital for preparedness, are based on very limited data. Most of the current knowledge has been gathered from a few pathosystems, whereas several hundred remain to be studied, probably including those that will become the center of the next epidemic. Unfortunately, aspects of a particular pathosystem are not always transferable to others. We recommend diversification of research topics of both fundamental and applied nature addressing multiple pathosystems. Increasing preparedness through knowledge acquisition is the best strategy to anticipate and manage diseases caused by this pathogen, described as "the most dangerous plant bacterium known worldwide."

Identification of habitat suitability for the dominant zoonotic tick species Haemaphysalis flava on Chongming Island, China

Haemaphysalis ticks are pathogenic vectors that threaten human and animal health and were identified in Chongming, the third largest island in China. To understand the distribution of these ticks and determine their potential invasion risk, this study aimed to identify the habitat suitability of the dominant tick H. flava based on natural environmental factors. Geographic information system (GIS) images were combined with sample points from tick investigations to map the spatial distribution of H. flava. Data on 19 bioclimatic variables, environmental variables, and satellite-based landscapes of Chongming Island were retrieved to create a landcover map related to natural environmental determinants of H. flava. These data included 38 sites associated with the vectors to construct species distribution models with MaxEnt, a model based on the maximum entropy principle, and to predict habitat suitability for H. flava on Chongming Island in 2050 and 2070 under different climate scenarios. The model performed well in predicting the H. flava distribution, with a training area under the curve of 0.84 and a test area under the curve of 0.73. A habitat suitability map of the whole study area was created for H. flava. The resulting map and natural environment analysis highlighted the importance of the normalized difference vegetation index and precipitation in the driest month for the bioecology of H. flava, with 141.61 km2 (11.77%), 282.94 km2 (23.35%), and 405.30 km2 (33.69%) of highly, moderately, and poorly suitable habitats, respectively. The distribution decreased by 135.55 km2 and 138.82 km2 in 2050 and 2070, respectively, under the shared socioeconomic pathway (SSP) 1.2.6 climate change scenario. However, under SSP 5.8.5, the total area will decrease by 128.5 km2 in 2050 and increase by 151.64 km2 in 2070. From a One Health perspective, this study provides good knowledge that will guide tick control efforts to prevent the spread of Haemaphysalis ticks or transmission risk of Haemaphysalis-borne infections at the human-animal-environment interface on the island.

Identifying key soil characteristics for Francisella tularensis classification with optimized Machine learning models

Francisella tularensis (Ft) poses a significant threat to both animal and human populations, given its potential as a bioweapon. Current research on the classification of this pathogen and its relationship with soil physical-chemical characteristics often relies on traditional statistical methods. In this study, we leverage advanced machine learning models to enhance the prediction of epidemiological models for soil-based microbes. Our model employs a two-stage feature ranking process to identify crucial soil attributes and hyperparameter optimization for accurate pathogen classification using a unique soil attribute dataset. Optimization involves various classification algorithms, including Support Vector Machines (SVM), Ensemble Models (EM), and Neural Networks (NN), utilizing Bayesian and Random search techniques. Results indicate the significance of soil features such as clay, nitrogen, soluble salts, silt, organic matter, and zinc , while identifying the least significant ones as potassium, calcium, copper, sodium, iron, and phosphorus. Bayesian optimization yields the best results, achieving an accuracy of 86.5% for SVM, 81.8% for EM, and 83.8% for NN. Notably, SVM emerges as the top-performing classifier, with an accuracy of 86.5% for both Bayesian and Random Search optimizations. The insights gained from employing machine learning techniques enhance our understanding of the environmental factors influencing Ft's persistence in soil. This, in turn, reduces the risk of false classifications, contributing to better pandemic control and mitigating socio-economic impacts on communities.

Climatic niche and range shifts of grey squirrels (Sciurus carolinensis Gmelin) in Europe: An invasive pest displacing native squirrels

BACKGROUND

As an invasive pest from North America, grey squirrels (GSs; Sciurus carolinensis Gmelin) are displacing native squirrels in Europe. However, the climatic niche and range dynamics of GSs in Europe remain largely unknown. Through niche and range dynamic models, we investigated climatic niche and range shifts between introduced GSs in Europe and native GSs in North America.

RESULTS

GSs in North America can survive in more variable climatic conditions and have much wider climatic niche breadth than do GSs in Europe. Based on climate, the potential range of GSs in Europe included primarily Britain, Ireland, and Italy, whereas the potential range of GSs in North America included vast regions of western and southern Europe. If GSs in Europe could occupy the same climatic niche space and potential range as GSs in North America, they would occupy an area ca. 2.45 times the size of their current range. The unfilling ranges of GSs in Europe relative to those of GSs in North America were primarily in France, Italy, Spain, Croatia, and Portugal.

CONCLUSION

Our observations implied that GSs in Europe have significant invasion potential, and that range projections based on their occurrence records in Europe may underestimate their invasion risk. Given that small niche shifts between GSs in Europe and in North America could lead to large range shifts, niche shifts could be a sensitive indicator in invasion risk assessment. The identified unfilling ranges of the GS in Europe should be prioritized in combating GS invasions in the future. © 2023 Society of Chemical Industry.

Assessing potential European areas of Pierce's disease mediated by insect vectors by using spatial ensemble model

Pierce's disease (PD) is a serious threat to grape production in Europe. This disease is caused by Xylella fastidiosa and is mediated by insect vectors, suggesting its high potential for spread and necessity for early monitoring. In this study, hence, potential distribution of Pierce's disease varied with climate change and was spatially evaluated in Europe using ensemble species distribution modeling. Two models of X. fastidiosa and three major insect vectors (Philaenus spumarius, Neophilaenus campestris, and Cicadella viridis) were developed using CLIMEX and MaxEnt. The consensus areas of the disease and insect vectors, along with host distribution, were evaluated using ensemble mapping to identify high-risk areas for the disease. Our predictions showed that the Mediterranean region would be the most vulnerable to Pierce's disease, and the high-risk area would increase three-fold due to climate change under the influence of N. campestris distribution. This study demonstrated a methodology for species distribution modeling specific to diseases and vectors while providing results that could be used for monitoring Pierce's disease by simultaneously considering the disease agent, vectors, and host distribution.

Shedding light on the effects of climate and anthropogenic pressures on the disappearance of Fagus sylvatica in the Italian lowlands: evidence from archaeo-anthracology and spatial analyses

Fagus sylvatica is one of the most representative trees of the European deciduous broadleaved forests, yet the impact of changing climatic conditions and anthropogenic pressures (anthromes) on its presence and distribution in the coastal and lowland areas of the Mediterranean Basin has long been overlooked. Here, we first analysed the local forest composition in two different time intervals (350-300 Before Current Era, BCE and 150-100 BCE) using charred wood remains from the Etruscan site of Cetamura (Tuscany, central Italy). Additionally, we reviewed all the relevant publications and the wood/charcoal data obtained from anthracological analysis in F. sylvatica, focusing on samples that date back to 4000 years before present, to better understand the drivers of beech presence and distribution during the Late Holocene (LH) in the Italian Peninsula. Then, we combined charcoal and spatial analyses to test the distribution of beech woodland at low elevation during LH in Italy and to evaluate the effect of climate change and/or anthrome on the disappearance of F. sylvatica from the lowlands. We collected 1383 charcoal fragments in Cetamura belonging to 21 woody taxa, with F. sylvatica being the most abundant species (28 %), followed by other broadleaved trees. We identified 25 sites in the Italian Peninsula with beech charcoals in the last 4000 years. Our spatial analyses showed a marked decrease in habitat suitability of F. sylvatica from LH to the present (ca. 48 %), particularly in the lowlands (0-300 m above sea level, a.s.l.) and in areas included between 300-600 m a.s.l. with a subsequent shift upwards of the beech woodland of ca. 200 m from the past to the present. In the lowland areas, where F. sylvatica has disappeared, anthrome alone and climate + anthorme had a main effect on beech distribution whitin 0-50 m a.s.l., while the climate from 50 to 300 m a.s.l. Furthermore, climate affect also the beech distrinution in the areas >300 m a.s.l., while climate + anthrome and antrhome alone were mainly focused on the lowland areas. Our results highlight the advantage of combining different approaches, such as charcoal analysis and spatial analyses, to explore biogeographic questions about the past and current distribution of F. sylvatica, with important implications for today's forest management and conservation policies.

Forecasting Pathogen Dynamics with Bayesian Model-Averaging: Application to Xylella fastidiosa

Forecasting invasive-pathogen dynamics is paramount to anticipate eradication and containment strategies. Such predictions can be obtained using a model grounded on partial differential equations (PDE; often exploited to model invasions) and fitted to surveillance data. This framework allows the construction of phenomenological but concise models relying on mechanistic hypotheses and real observations. However, it may lead to models with overly rigid behavior and possible data-model mismatches. Hence, to avoid drawing a forecast grounded on a single PDE-based model that would be prone to errors, we propose to apply Bayesian model averaging (BMA), which allows us to account for both parameter and model uncertainties. Thus, we propose a set of different competing PDE-based models for representing the pathogen dynamics, we use an adaptive multiple importance sampling algorithm (AMIS) to estimate parameters of each competing model from surveillance data in a mechanistic-statistical framework, we evaluate the posterior probabilities of models by comparing different approaches proposed in the literature, and we apply BMA to draw posterior distributions of parameters and a posterior forecast of the pathogen dynamics. This approach is applied to predict the extent of Xylella fastidiosa in South Corsica, France, a phytopathogenic bacterium detected in situ in Europe less than 10 years ago (Italy 2013, France 2015). Separating data into training and validation sets, we show that the BMA forecast outperforms competing forecast approaches.

1H-NMR Metabolomics Study after Foliar and Endo-Therapy Treatments of Xylella fastidiosa subsp. pauca Infected Olive Trees: Medium Time Monitoring of Field Experiments

Here we report the medium-term effects of foliar spray and endo-therapy treatments with different doses of a Cu/Zn citric acid biocomplex (Dentamet®) in Xylella fastidiosa infected olive trees of Salento, Apulia region (South-east Italy). Leaf extract samples from field-treated 150 years old olive trees cvs Ogliarola salentina and Cellina di Nardò were studied by 1H NMR-based metabolomics. The result of different applications of Dentamet® endo-therapy after 60, 120 and 180 days in comparison with traditional foliar spray treatment and water injection as a control have been investigated. The metabolic profile analyses, performed by 1H NMR-based metabolomic approach, indicated plant metabolites variations connected to the disease progression such as mannitol, quinic acid, and oleuropein related compounds. The best results, in terms of discrimination of the metabolic profiles with respect to water injection, were found for monthly endo-therapy treatments. Dentamet® foliar application demonstrated more specific time related progressive effectiveness with respect to intravascular treatments. Therefore, besides a possible more effective performance of endo-therapy with respect to foliar treatments, the need of further doses/frequencies trimming to obtain long-term results was also assessed. The present field studies confirmed the indication of Dentamet® effectiveness in metabolic variation induction, potentially linked with reducing the X. fastidiosa subspecies pauca related Olive Quick Decline Syndrome (OQDS) symptoms development.

Spatial analysis of the occurrence of the western conifer seed bug Leptoglossus occidentalis (Heteroptera: Coreidae) in Europe based on multiple environmental variables

The western conifer seed bug (WCSB) Leptoglossus occidentalis (Heidemann) (Heteroptera: Coreidae) is a pest insect that causes significant losses of coniferous trees worldwide. In this study, we sought to project the potential distribution of the WCSB based on dual CLIMEX modeling and random forest (RF) analysis to obtain basic data for WCSB monitoring strategies. The CLIMEX model, a semimechanistic niche model that responds to climate-based environmental parameters, is a species distribution model that focuses on regional climatic suitability. Given that this model can be used to select areas that are likely to reflect the climatically favorable spread of species, which we initially used CLIMEX to evaluate the potential distribution of the WCSB. The RF algorithm was used to predict the potential occurrence of WCSB and to evaluate the relative importance of environmental variables for WCSB occurrence. Using the RF model, land cover was found to be the most important variable for classifying the presence/pseudo-absence of the WCSB, with an accuracy of 77.1%. Climatic suitability for the WCSB was predicted to be 2.4-fold higher in Southern Europe than in Western Europe, and the WCSB was predicted to occur primarily near coniferous forests. Given that CLIMEX and RF analyses yielded different prediction results, using the findings of both models may compensate for the shortcomings of these models when used independently. Consequently, to ensure greater prediction reliability, we believe that it would be beneficial to base predictions on the combined potential distribution data obtained using both modeling approaches.

Potentially Suitable Geographical Area for Monochamus alternatus under Current and Future Climatic Scenarios Based on Optimized MaxEnt Model

M. alternatus is considered to be an important and effective insect vector for the spread of the important international forest quarantine pest, Bursaphelenchus xylophilus. The precise determination of potential suitable areas of M. alternatus is essential to monitor, prevent, and control M. alternatus worldwide. According to the distribution points and climatic variables, the optimized MaxEnt model and ArcGIS were used to predict the current and future potentially suitable areas of M. alternatus worldwide. The optimized MaxEnt model parameters were set as feature combination (FC) = LQHP and β = 1.5, which were determined by the values of AUC_diff, OR₁₀, and ΔAICc. Bio2, Bio6, Bio10, Bio12, and Bio14 were the dominant bioclimatic variables affecting the distribution of M. alternatus. Under the current climate conditions, the potentially suitable habitats of M. alternatus were distributed across all continents except Antarctica, accounting for 4.17% of the Earth's total land area. Under future climate scenarios, the potentially suitable habitats of M. alternatus increased significantly, spreading to a global scale. The results of this study could provide a theoretical basis for the risk analysis of the global distribution and dispersal of M. alternatus as well as the precise monitoring and prevention of this beetle.

Two phase feature-ranking for new soil dataset for Coxiella burnetii persistence and classification using machine learning models

Coxiella burnetii (Cb) is a hardy, stealth bacterial pathogen lethal for humans and animals. Its tremendous resistance to the environment, ease of propagation, and incredibly low infectious dosage make it an attractive organism for biowarfare. Current research on the classification of Coxiella and features influencing its presence in the soil is generally confined to statistical techniques. Machine learning other than traditional approaches can help us better predict epidemiological modeling for this soil-based pathogen of public significance. We developed a two-phase feature-ranking technique for the pathogen on a new soil feature dataset. The feature ranking applies methods such as ReliefF (RLF), OneR (ONR), and correlation (CR) for the first phase and a combination of techniques utilizing weighted scores to determine the final soil attribute ranks in the second phase. Different classification methods such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) have been utilized for the classification of soil attribute dataset for Coxiella positive and negative soils. The feature-ranking methods established that potassium, chromium, cadmium, nitrogen, organic matter, and soluble salts are the most significant attributes. At the same time, manganese, clay, phosphorous, copper, and lead are the least contributing soil features for the prevalence of the bacteria. However, potassium is the most influential feature, and manganese is the least significant soil feature. The attribute ranking using RLF generates the most promising results among the ranking methods by generating an accuracy of 80.85% for MLP, 79.79% for LR, and 79.8% for LDA. Overall, SVM and MLP are the best-performing classifiers, where SVM yields an accuracy of 82.98% and 81.91% for attribute ranking by CR and RLF; and MLP generates an accuracy of 76.60% for ONR. Thus, machine models can help us better understand the environment, assisting in the prevalence of bacteria and decreasing the chances of false classification. Subsequently, this can assist in controlling epidemics and alleviating the devastating effect on the socio-economics of society.

Predicting the Impact of Climate Change on the Distribution of a Neglected Arboviruses Vector (Armigeres subalbatus) in China

The geographic boundaries of arboviruses continue to expand, posing a major health threat to millions of people around the world. This expansion is related to the availability of effective vectors and suitable habitats. Armigeres subalbatus (Coquillett, 1898), a common and neglected species, is of increasing interest given its potential vector capacity for Zika virus. However, potential distribution patterns and the underlying driving factors of Ar. subalbatus remain unknown. In the current study, detailed maps of their potential distributions were developed under both the current as well as future climate change scenarios (SSP126 and SSP585) based on CMIP6 data, employing the MaxEnt model. The results showed that the distribution of the Ar. subalbatus was mainly affected by temperature. Mean diurnal range was the strongest predictor in shaping the distribution of Ar. subalbatus, with an 85.2% contribution rate. By the 2050s and 2070s, Ar. subalbatus will have a broader potential distribution across China. There are two suitable expansion types under climate change in the 2050s and 2070s. The first type is continuous distribution expansion, and the second type is sporadic distribution expansion. Our comprehensive analysis of Ar. subalbatus’s suitable distribution areas shifts under climate change and provides useful and insightful information for developing management strategies for future arboviruses.

Modelling the current and future potential distribution of the bean bug Riptortus pedestris with increasingly serious damage to soybean

BACKGROUND

The bean bug, Riptortus pedestris, has received intense attention in recent years because of its involvement in increasing outbreaks of staygreen syndrome in soybean (Glycine max (L.)), often causing almost 100% loss of soybean yield in China. However, for this pest of great economic importance, potential current and future distribution patterns and their underlying driving factors remain unclear.

RESULTS

Maxent modelling under climate, elevation and land-use (including the distribution information of G. max) variables showed that the current potential distribution covered a vast geographic range, primarily including most parts of south, South East and east Asia. Under future environmental scenarios, suitable habitat expanded markedly. Areas that would become highly suitable for R. pedestris were primarily located in north-east China and west India. Five bioclimatic (BIO13, BIO08, BIO18, BIO02 and BIO07) and one land-use (C3 annual crops) predictors contributed approximately 95% to the modelling, and analyses of curve responses showed that to a certain extent, R. pedestris preferred relatively high temperature and precipitation. Our results indicate that a high risk of R. pedestris outbreaks is present in parts of Asia, especially in the soybean-growing regions of China, and this risk will continue in the future.

CONCLUSION

The predicted distribution pattern and key regulating factors identified herein could provide a vital reference for developing pest management policies and further alleviate the incidence of staygreen syndrome in soybean. © 2022 Society of Chemical Industry.

Predicting potential global and future distributions of the African armyworm (Spodoptera exempta) using species distribution models

Invasive species have historically been a problem derived from global trade and transport. To aid in the control and management of these species, species distribution models (SDMs) have been used to help predict possible areas of expansion. Our focal organism, the African Armyworm (AAW), has historically been known as an important pest species in Africa, occurring at high larval densities and causing outbreaks that can cause enormous economic damage to staple crops. The goal of this study is to map the AAW’s present and potential distribution in three future scenarios for the region, and the potential global distribution if the species were to invade other territories, using 40 years of data on more than 700 larval outbreak reports from Kenya and Tanzania. The present distribution in East Africa coincides with its previously known distribution, as well as other areas of grassland and cropland, which are the host plants for this species. The different future climatic scenarios show broadly similar potential distributions in East Africa to the present day. The predicted global distribution shows areas where the AAW has already been reported, but also shows many potential areas in the Americas where, if transported, environmental conditions are suitable for AAW to thrive and where it could become an invasive species.

Ecological niche modeling based on ensemble algorithms to predicting current and future potential distribution of African swine fever virus in China

African swine fever (ASF) is a tick-borne infectious disease initially described in Shenyang province China in 2018 but is now currently present nationwide. ASF has high infectivity and mortality rates, which often results in transportation and trade bans, and high expenses to prevent and control the, hence causing huge economic losses and a huge negative impact on the Chinese pig farming industry. Ecological niche modeling has long been adopted in the epidemiology of infectious diseases, in particular vector-borne diseases. This study aimed to establish an ecological niche model combined with data from ASF incidence rates in China from August 2018 to December 2021 in order to predict areas for African swine fever virus (ASFV) distribution in China. The model was developed in R software using the biomod2 package and ensemble modeling techniques. Environmental and topographic variables included were mean diurnal range (°C), isothermality, mean temperature of wettest quarter (°C), precipitation seasonality (cv), mean precipitation of warmest quarter(mm), mean precipitation of coldest quarter (mm), normalized difference vegetation index, wind speed (m/s), solar radiation (kJ /day), and elevation/altitude (m). Contribution rates of the variables normalized difference vegetation index, mean temperature of wettest quarter, mean precipitation of coldest quarter, and mean precipitation of warmest quarter were, respectively, 47.61%, 28.85%, 10.85%, and 7.27% (according to CA), which accounted for over 80% of contribution rates related to variables. According to model prediction, most of areas revealed as suitable for ASF distribution are located in the southeast coast or central region of China, wherein environmental conditions are suitable for soft ticks’ survival. In contrast, areas unsuitable for ASFV distribution in China are associated with arid climate and poor vegetation, which are less conducive to soft ticks’ survival, hence to ASFV transmission. In addition, prediction spatial suitability for future ASFV distribution suggests narrower areas for ASFV spread. Thus, the ensemble model designed herein could be used to conceive more efficient prevention and control measure against ASF according to different geographical locations in China.

Landscape and Vegetation Patterns Zoning Is a Methodological Tool for Management Costs Implications Due to Xylella fastidiosa Invasion

Philaenus spumarius (Linnaeus 1758, hereafter Ps) is considered one of the main insect vectors responsible for the spread of an alien biota, Xylella fastidiosa (Wells 1987, hereafter Xf), in the Salento area, Apulia region (Southern Italy). Effective management of this biological invader depends on the continuous surveillance and monitoring of its insect vector. As such, this research elicits the invasion drivers (i.e., landscape and vegetation indicators) that influence the abundance and the dynamics of this vector and, consequently, the spatial spread of this bacterium in this Italian region. For this purpose, a spatial pattern clustering methodological approach is considered. The results reveal that spatial variation and territorial differentiation may differ from zone to zone in the same invaded area, for which effective management and monitoring planning should be addressed. Further, six agro-ecosystems zones have been identified with respect to five indicators: (i) vegetation index, (ii) intensity of cultivation, (iii) cultural diversity, (iv) density of agricultural landscape elements, and (v) altitude. This paper has public implications and contributes to an understanding of how zoning of an infected area, by an alien biota, into homogenous zones may impact its effective management costs. This approach could also be applied in other countries affected or potentially affected by the phenomenon of Xf invasion.

Prediction of the potential distribution of the predatory mite Neoseiulus californicus (McGregor) in China under current and future climate scenarios

Neoseiulus californicus is a predatory mite with a wide global distribution that can effectively control a variety of pest mites. In this study, MaxEnt was used to analyse the potential distribution of N. californicus in China and the BCC-CSM2-MR model was used to predict changes in the suitable areas for the mite from 2021 to 2100 under the scenarios of SSP126, SSP245 and SSP585. The results showed that (1) the average of area under curve value of the model was over 0.95, which demonstrated an excellent model accuracy. (2) Annual mean temperature (Bio1), precipitation of coldest quarter (Bio19), and precipitation of driest quarter (Bio17) were the main climatic variables that affected and controlled the potential distribution of N. californicus, with suitable ranges of 6.97–23.27 °C, 71.36–3924.8 mm, and 41.94–585.08 mm, respectively. (3) The suitable areas for N. californicus were mainly distributed in the southern half of China, with a total suitable area of 226.22 × 10⁴ km² in current. Under the future climate scenario, compared with the current scenario, lowly and moderately suitable areas of N. californicus increased, while highly suitable areas decreased. Therefore, it may be necessary to cultivate high-temperature resistant strains of N. californicus to adapt to future environmental changes.

Prediction of the potential distribution pattern of the great gerbil (Rhombomys opimus) under climate change based on ensemble modelling

BACKGROUND

Rodent infestation is a global biological problem. Rodents are widely distributed worldwide, cause harm to agriculture, forestry, and animal husbandry production and spread a variety of natural focal diseases. In this study, 10 ecological niche models were combined into an ensemble model to assess the distribution of suitable habitats for Rhombomys opimus and to predict the impact of future climate change on the distribution of R. opimus under low, medium and high socioeconomic pathway scenarios of CMIP6.

RESULTS

In general, with the exception of extreme climates (2090-SSP585), the current and potential future ranges of R. opimus habitat are maintained at approximately 220 × 10⁴  km² . In combination with human footprint data, the potential distribution area of R. opimus was found to coincide with areas with a moderate human footprint. In addition, this distribution area will gradually shift to higher-latitude regions, and the suitable habitat area of R. opimus will gradually shrink in China, Iran, Afghanistan, and Turkmenistan while increasing in Mongolia and Kazakhstan.

CONCLUSIONS

These results help identify the impact of climate change on the potential distribution of R. opimus and provide supportive information for the development of management strategies to protect against future ecological and human health risks. © 2022 Society of Chemical Industry.

Widespread occurrence of Batrachochytrium dendrobatidis in Ontario, Canada, and predicted habitat suitability for the emerging Batrachochytrium salamandrivorans

Chytridiomycosis, caused by the fungi Batrachochytrium dendrobatidis and Batrachochytrium salamandrivorans, is associated with massive amphibian mortality events worldwide and with some species’ extinctions. Previous ecological niche models suggest that B. dendrobatidis is not well‐suited to northern, temperate climates, but these predictions have often relied on datasets in which northern latitudes are underrepresented. Recent northern detections of B. dendrobatidis suggest that these models may have underestimated the suitability of higher latitudes for this fungus. We used qPCR to test for B. dendrobatidis in 1,041 non‐invasive epithelial swab samples from 18 species of amphibians collected across 735,345 km² in Ontario and Akimiski Island (Nunavut), Canada. We detected the pathogen in 113 samples (10.9%) from 11 species. Only one specimen exhibited potential clinical signs of disease. We used these data to produce six Species Distribution Models of B. dendrobatidis, which classified half of the study area as potential habitat for the fungus. We also tested each sample for B. salamandrivorans, an emerging pathogen that is causing alarming declines in European salamanders, but is not yet detected in North America. We did not detect B. salamandrivorans in any of the samples, providing a baseline for future surveillance. We assessed the potential risk of future introduction by comparing salamander richness to temperature‐dependent mortality, predicted by a previous exposure study. Areas with the highest species diversity and predicted mortality risk extended 60,530 km² across southern Ontario, highlighting the potential threat B. salamandrivorans poses to northern Nearctic amphibians. Preventing initial introduction will require coordinated, transboundary regulation of trade in amphibians (including frogs that can carry and disperse B. salamandrivorans), and surveillance of the pathways of introduction (e.g., water and wildlife). Our results can inform surveillance for both pathogens and efforts to mitigate the spread of chytridiomycosis through wild populations.

Large-scale forecasting of Heracleum sosnowskyi habitat suitability under the climate change on publicly available data

This research aims to establish the possible habitat suitability of Heracleum sosnowskyi (HS), one of the most aggressive invasive plants, in current and future climate conditions across the territory of the European part of Russia. We utilised a species distribution modelling framework using publicly available data of plant occurrence collected in citizen science projects (CSP). Climatic variables and soil characteristics were considered to follow possible dependencies with environmental factors. We applied Random Forest to classify the study area. We addressed the problem of sampling bias in CSP data by optimising the sampling size and implementing a spatial cross-validation scheme. According to the Random Forest model built on the finally selected data shape, more than half of the studied territory in the current climate corresponds to a suitability prediction score higher than 0.25. The forecast of habitat suitability in future climate was highly similar for all climate models. Almost the whole studied territory showed the possibility for spread with an average suitability score of 0.4. The mean temperature of the wettest quarter and precipitation of wettest month demonstrated the highest influence on the HS distribution. Thus, currently, the whole study area, excluding the north, may be considered as s territory with a high risk of HS spreading, while in the future suitable locations for the HS habitat will include high latitudes. We showed that chosen geodata pre-processing, and cross-validation based on geospatial blocks reduced significantly the sampling bias. Obtained predictions could help to assess the risks accompanying the studied plant invasion capturing the patterns of the spread, and can be used for the conservation actions planning.

Ecological niche modeling predicting the potential distribution of African horse sickness virus from 2020 to 2060

African horse sickness is a vector-borne, non-contagious and highly infectious disease of equines caused by African horse sickness viruses (AHSv) that mainly affect horses. The occurrence of the disease causes huge economic impacts because of its high fatality rate, trade ban and disease control costs. In the planning of vectors and vector-borne diseases like AHS, the application of Ecological niche models (ENM) used an enormous contribution in precisely delineating the suitable habitats of the vector. We developed an ENM to delineate the global suitability of AHSv based on retrospective outbreak data records from 2005 to 2019. The model was developed in an R software program using the Biomod2 package with an Ensemble modeling technique. Predictive environmental variables like mean diurnal range, mean precipitation of driest month(mm), precipitation seasonality (cv), mean annual maximum temperature (^oc), mean annual minimum temperature (^oc), mean precipitation of warmest quarter(mm), mean precipitation of coldest quarter (mm), mean annual precipitation (mm), solar radiation (kj /day), elevation/altitude (m), wind speed (m/s) were used to develop the model. From these variables, solar radiation, mean maximum temperature, average annual precipitation, altitude and precipitation seasonality contributed 36.83%, 17.1%, 14.34%, 7.61%, and 6.4%, respectively. The model depicted the sub-Sahara African continent as the most suitable area for the virus. Mainly Senegal, Burkina Faso, Niger, Nigeria, Ethiopia, Sudan, Somalia, South Africa, Zimbabwe, Madagascar and Malawi are African countries identified as highly suitable countries for the virus. Besides, OIE-listed disease-free countries like India, Australia, Brazil, Paraguay and Bolivia have been found suitable for the virus. This model can be used as an epidemiological tool in planning control and surveillance of diseases nationally or internationally.

Accuracy of a LiDAR-Based Individual Tree Detection and Attribute Measurement Algorithm Developed to Inform Forest Products Supply Chain and Resource Management

Individual Tree Detection (ITD) algorithms that use Airborne Laser Scanning (ALS) data can provide accurate tree locations and measurements of tree-level attributes that are required for stand-to-landscape scale forest inventory and supply chain management. While numerous ITD algorithms exist, few have been assessed for accuracy in stands with complex forest structure and composition, limiting their utility for operational application. In this study, we conduct a preliminary assessment of the ability of the ForestView® algorithm created by Northwest Management Incorporated to detect individual trees, classify tree species, live/dead status, canopy position, and estimate height and diameter at breast height (DBH) in a mixed coniferous forest with an average tree density of 543 (s.d. ±387) trees/hectare. ITD accuracy was high in stands with lower canopy cover (recall: 0.67, precision: 0.8) and lower in stands with higher canopy cover (recall: 0.36, precision: 0.67), mainly owing to omission of suppressed trees that were not detected under the dominant tree canopy. Tree species that were well-represented within the study area had high classification accuracies (producer’s/user’s accuracies > ~60%). The similarity between the ALS estimated and observed tree attributes was high, with no statistical difference in the ALS estimated height and DBH distributions and the field observed height and DBH distributions. RMSEs for tree-level height and DBH were 0.69 m and 7.2 cm, respectively. Overall, this algorithm appears comparable to other ITD and measurement algorithms, but quantitative analyses using benchmark datasets in other forest types and cross-comparisons with other ITD algorithms are needed.

Uncertainty, Complexity and Constraints: How Do We Robustly Assess Biological Responses under a Rapidly Changing Climate?

How robust is our assessment of impacts to ecosystems and species from a rapidly changing climate during the 21st century? We examine the challenges of uncertainty, complexity and constraints associated with applying climate projections to understanding future biological responses. This includes an evaluation of how to incorporate the uncertainty associated with different greenhouse gas emissions scenarios and climate models, and constraints of spatiotemporal scales and resolution of climate data into impact assessments. We describe the challenges of identifying relevant climate metrics for biological impact assessments and evaluate the usefulness and limitations of different methodologies of applying climate change to both quantitative and qualitative assessments. We discuss the importance of incorporating extreme climate events and their stochastic tendencies in assessing ecological impacts and transformation, and provide recommendations for better integration of complex climate–ecological interactions at relevant spatiotemporal scales. We further recognize the compounding nature of uncertainty when accounting for our limited understanding of the interactions between climate and biological processes. Given the inherent complexity in ecological processes and their interactions with climate, we recommend integrating quantitative modeling with expert elicitation from diverse disciplines and experiential understanding of recent climate-driven ecological processes to develop a more robust understanding of ecological responses under different scenarios of future climate change. Inherently complex interactions between climate and biological systems also provide an opportunity to develop wide-ranging strategies that resource managers can employ to prepare for the future.

Predicting the current and future global distribution of the invasive freshwater hydrozoan Craspedacusta sowerbii

The freshwater jellyfish Craspedacusta sowerbii is one of the most widespread invasive species, but its global distribution remains uncertain due to ephemeral appearances and general lack of information in various aquatic environments. The aim of this study was to map current and future distributions (2050 and 2100) using Species Distribution Models allowing to visualize the habitat suitability and make projections of its changes under potential climate change scenarios. Except in Oceania where the range decreased, an expansion of C. sowerbii was projected during the next century under modeled future scenarios being most intensive during the first half of the century. The present study shows that the expansion of C. sowerbii worldwide would be facilitated mainly by precipitation, vapor pressure, and temperature. The predictions showed that this species over the eighty years will invade high-latitude regions in both hemispheres with ecological consequences in already threatened freshwater ecosystems.

Driven to the edge: Species distribution modeling of a Clawed Salamander (Hynobiidae: Onychodactylus koreanus) predicts range shifts and drastic decrease of suitable habitats in response to climate change

Climate change is one of the major threats to global amphibian diversity, and consequently, the species distribution is expected to shift considerably in the future. Therefore, predicting such shifts is important to guide conservation and management plans. Here, we used eight independent environmental variables and four representative concentration pathways (RCPs) to model the current and future habitat suitability of the Korean clawed salamander (Onychodactylus koreanus) and then defined the dispersal limits of the species using cost distance analysis. The current habitat suitability model generated using the maximum entropy algorithm was highly consistent with the known distribution of the species and had good predictive performance. Projections onto years 2050 and 2070 predicted a drastic decrease of habitat suitability across all RCPs, with up to 90.1% decrease of suitable area and 98.0% decrease of optimal area predicted from binary presence grids. The models also predicted a northeastward shift of habitat suitability toward high-elevation areas and a persistence of suitability along the central ridge of the Baekdudaegan Range. This area is likely to become a climatic refugium for the species in the future, and it should be considered as an area of conservation priority. Therefore, we urge further ecological studies and population monitoring to be conducted across the range of O. koreanus. The vulnerability to rapid climate change is also shared by other congeneric species, and assessing the impacts of climate change on these other species is needed to better conserve this unique lineage of salamanders.

Global distribution of soapberries (Sapindus L.) habitats under current and future climate scenarios

Sapindus (Sapindus L.) is a widely distributed economically important tree genus that provides biodiesel, biomedical and biochemical products. However, with climate change, deforestation, and economic development, the diversity of Sapindus germplasms may face the risk of destruction. Therefore, utilising historical environmental data and future climate projections from the BCC-CSM2-MR global climate database, we simulated the current and future global distributions of suitable habitats for Sapindus using a Maximum Entropy (MaxEnt) model. The estimated ecological thresholds for critical environmental factors were: a minimum temperature of 0-20 °C in the coldest month, soil moisture levels of 40-140 mm, a mean temperature of 2-25 °C in the driest quarter, a mean temperature of 19-28 °C in the wettest quarter, and a soil pH of 5.6-7.6. The total suitable habitat area was 6059.97 × 10⁴ km², which was unevenly distributed across six continents. As greenhouse gas emissions increased over time, the area of suitable habitats contracted in lower latitudes and expanded in higher latitudes. Consequently, surveys and conservation should be prioritised in southern hemisphere areas which are in danger of becoming unsuitable. In contrast, other areas in northern and central America, China, and India can be used for conservation and large-scale cultivation in the future.

Artificial illumination influences niche segregation in bats

Artificial light at night (ALAN) is a pervasive form of pollution largely affecting wildlife, from individual behaviour to community structure and dynamics. As nocturnal mammals, bats are often adversely affected by ALAN, yet some "light-opportunistic" species exploit it by hunting insects swarming near lights. Here we used two potentially competing pipistrelle species as models, Kuhl's (Pipistrellus kuhlii) and common (Pipistrellus pipistrellus) pipistrelles, both known to forage in artificially illuminated areas. We set our study in a mountainous area of central Italy, where only recently did the two species become syntopic. We applied spatial modelling and radiotracking to contrast potential vs. actual environmental preferences by the two pipistrelles. Species distribution models and niche analysis showed a large interspecific niche overlap, including a preference for illuminated areas, presenting a potential competition scenario. Pipistrellus pipistrellus association with ALAN, however, was weakened by adding P. kuhlii as a biotic variable to the model. Radiotracking showed that the two species segregated habitats at a small spatial scale and that P. kuhlii used artificially illuminated sites much more frequently than P. pipistrellus, despite both species potentially being streetlamp foragers. We demonstrate that ALAN influences niche segregation between two potentially competing species, confirming its pervasive effects on species and community dynamics, and provide an example of how light pollution and species' habitat preferences may weave a tapestry of complex ecological interactions.

Assessment of Ionomic, Phenolic and Flavonoid Compounds for a Sustainable Management of Xylella fastidiosa in Morocco

Morocco belongs to the countries ranked at a high-risk level for entry, establishment, and spread of Xylella fastidiosa, which has recently re-emerged as a plant pathogen of global importance causing olive quick decline syndrome (OQDS). Symptomatic infection by X. fastidiosa leads to devastating diseases and important economic losses. To prevent such losses and damages, countries without current outbreaks like Morocco need to first understand their host plant responses to X. fastidiosa. The assessment of the macro and micro-elements content (ionome) in leaves can give basic and useful information along with being a powerful tool for the sustainable management of diseases caused by this devastating pathogen. Herein, we compare the leaf ionome of four important autochthonous Moroccan olive cultivars (‘Picholine Marocaine’, ‘Haouzia’, ‘Menara’, and ‘Meslalla’), and eight Mediterranean varieties introduced in Morocco (‘Arbequina’, ‘Arbosana’, ‘Leccino’, ‘Ogliarola salentina’, ‘Cellina di Nardo’, ‘Frantoio’, ‘Leucocarpa’, and ‘Picholine de Languedoc’), to develop hypotheses related to the resistance or susceptibility of the Moroccan olive trees to X. fastidiosa infection. Leaf ionomes, mainly Ca, Cu, Fe, Mg, Mn, Na, Zn, and P, were determined using inductively coupled plasma optical emission spectroscopy (ICP-OES). These varieties were also screened for their total phenolics and flavonoids content. Data were then involved in a comparative scheme to determine the plasticity of the pathogen. Our results showed that the varieties ‘Leccino’, ’Arbosana’, ‘Arbequina’ consistently contained higher Mn, Cu, and Zn and lower Ca and Na levels compared with the higher pathogen-sensitive ‘Ogliarola salentina’ and ‘Cellina di Nardò’. Our findings suggest that ‘Arbozana’, ‘Arbiquina’, ‘Menara’, and ‘Haouzia’ may tolerate the infection by X. fastidiosa to varying degrees, provides additional support for ‘Leccino’ having resistance to X. fastidiosa, and suggests that both ‘Ogliarola salentina’ and ‘Cellina di Nardö’ are likely sensitive to X. fastidiosa infection.

Mapping habitat suitability for Asiatic black bear and red panda in Makalu Barun National Park of Nepal from Maxent and GARP models

Habitat evaluation is essential for managing wildlife populations and formulating conservation policies. With the rise of innovative powerful statistical techniques in partnership with Remote Sensing, GIS and GPS techniques, spatially explicit species distribution modeling (SDM) has rapidly grown in conservation biology. These models can help us to study habitat suitability at the scale of the species range, and are particularly useful for examining the overlapping habitat between sympatric species. Species presence points collected through field GPS observations, in conjunction with 13 different topographic, vegetation related, anthropogenic, and bioclimatic variables, as well as a land cover map with seven classification categories created by support vector machine (SVM) were used to implement Maxent and GARP ecological niche models. With the resulting ecological niche models, the suitable habitat for asiatic black bear (Ursus thibetanus) and red panda (Ailurus fulgens) in Nepal Makalu Barun National Park (MBNP) was predicted. All of the predictor variables were extracted from freely available remote sensing and publicly shared government data resources. The modeled results were validated by using an independent dataset. Analysis of the regularized training gain showed that the three most important environmental variables for habitat suitability were distance to settlement, elevation, and mean annual temperature. The habitat suitability modeling accuracy, characterized by the mean area under curve, was moderate for both species when GARP was used (0.791 for black bear and 0.786 for red panda), but was moderate for black bear (0.857), and high for red panda (0.920) when Maxent was used. The suitable habitat estimated by Maxent for black bear and red panda was 716 km2 and 343 km2 respectively, while the suitable area determined by GARP was 1074 km2 and 714 km2 respectively. Maxent predicted that the overlapping area was 83% of the red panda habitat and 40% of the black bear habitat, while GARP estimated 88% of the red panda habitat and 58% of the black bear habitat overlapped. The results of land cover exhibited that barren land covered the highest percentage of area in MBNP (36.0%) followed by forest (32.6%). Of the suitable habitat, both models indicated forest as the most preferred land cover for both species (63.7% for black bear and 61.6% for red panda from Maxent; 59.9% black bear and 58.8% for red panda from GARP). Maxent outperformed GARP in terms of habitat suitability modeling. The black bear showed higher habitat selectivity than red panda. We suggest that proper management should be given to the overlapping habitats in the buffer zone. For remote and inaccessible regions, the proposed methods are promising tools for wildlife management and conservation, deserving further popularization.

MaxEnt Modeling Based on CMIP6 Models to Project Potential Suitable Zones for Cunninghamia lanceolata in China

Cunninghamia lanceolata (Lamb.) Hook. (Chinese fir) is one of the main timber species in Southern China, which has a wide planting range that accounts for 25% of the overall afforested area. Moreover, it plays a critical role in soil and water conservation; however, its suitability is subject to climate change. For this study, the appropriate distribution area of C. lanceolata was analyzed using the MaxEnt model based on CMIP6 data, spanning 2041–2060. The results revealed that (1) the minimum temperature of the coldest month (bio6), and the mean diurnal range (bio2) were the most important environmental variables that affected the distribution of C. lanceolata; (2) the currently suitable areas of C. lanceolata were primarily distributed along the southern coastal areas of China, of which 55% were moderately so, while only 18% were highly suitable; (3) the projected suitable area of C. lanceolata would likely expand based on the BCC-CSM2-MR, CanESM5, and MRI-ESM2-0 under different SSPs spanning 2041–2060. The increased area estimated for the future ranged from 0.18 to 0.29 million km2, where the total suitable area of C. lanceolata attained a maximum value of 2.50 million km2 under the SSP3-7.0 scenario, with a lowest value of 2.39 million km2 under the SSP5-8.5 scenario; (4) in combination with land use and farmland protection policies of China, it is estimated that more than 60% of suitable land area could be utilized for C. lanceolata planting from 2041–2060 under different SSP scenarios. Although climate change is having an increasing influence on species distribution, the deleterious impacts of anthropogenic activities cannot be ignored. In the future, further attention should be paid to the investigation of species distribution under the combined impacts of climate change and human activities.

Combining modern tracking data and historical records improves understanding of the summer habitats of the Eastern Lesser White-fronted Goose Anser erythropus

The Lesser White-fronted Goose (Anser erythropus), smallest of the "gray" geese, is listed as Vulnerable on the IUCN Red List and protected in all range states. There are three populations, with the least studied being the Eastern population, shared between Russia and China. The extreme remoteness of breeding enclaves makes them largely inaccessible to researchers. As a substitute for visitation, remotely tracking birds from wintering grounds allows exploration of their summer range. Over a period of three years, and using highly accurate GPS tracking devices, eleven individuals of A. erythropus were tracked from the key wintering site of China, to summering, and staging sites in northeastern Russia. Data obtained from that tracking, bolstered by ground survey and literature records, were used to model the summer distribution of A. erythropus. Although earlier literature describes a patchy summer range, the model suggests a contiguous summer habitat range is possible, although observations to date cannot confirm A. erythropus is present throughout the modeled range. The most suitable habitats are located along the coasts of the Laptev Sea, primarily the Lena Delta, in the Yana-Kolyma Lowland, and smaller lowlands of Chukotka with narrow riparian extensions upstream along major rivers such as the Lena, Indigirka, and Kolyma. The probability of A. erythropus presence is related to areas with altitude less than 500 m with abundant wetlands, especially riparian habitat, and a climate with precipitation of the warmest quarter around 55 mm and mean temperature around 14°C during June-August. Human disturbance also affects site suitability, with a gradual decrease in species presence starting around 160 km from human settlements. Remote tracking of animal species can bridge the knowledge gap required for robust estimation of species distribution patterns in remote areas. Better knowledge of species' distribution is important in understanding the large-scale ecological consequences of rapid global change and establishing conservation management strategies.

Past, present, and future geographic range of the relict Mediterranean and Macaronesian Juniperus phoenicea complex

AIM

The aim of this study is to model the past, current, and future distribution of J. phoenicea s.s., J. turbinata, and J. canariensis, based on bioclimatic variables using a maximum entropy model (Maxent) in the Mediterranean and Macaronesian regions.

LOCATION

Mediterranean and Macaronesian.

TAXON

Cupressaceae, Juniperus.

METHODS

Data on the occurrence of the J. phoenicea complex were obtained from the Global Biodiversity Information Facility (GBIF.org), the literature, herbaria, and the authors' field notes. Bioclimatic variables were obtained from the WorldClim database and Paleoclim. The climate data related to species localities were used for predictions of niches by implementation of Maxent, and the model was evaluated with ENMeval.

RESULTS

The potential niches of Juniperus phoenicea during the Last Interglacial period (LIG), Last Glacial Maximum climate (LGM), and Mid-Holocene (MH) covered 30%, 10%, and almost 100%, respectively, of the current potential niche. Climate warming may reduce potential niches by 30% in RCP2.6 and by 90% in RCP8.5. The potential niches of Juniperus turbinata had a broad circum-Mediterranean and Canarian distribution during the LIG and the MH; its distribution extended during the LGM when it was found in more areas than at present. The predicted warming in scenarios RCP2.6 and RCP8.5 could reduce the current potential niche by 30% and 50%, respectively. The model did not find suitable niches for J. canariensis during the LIG and the LGM, but during the MH its potential niche was 30% larger than at present. The climate warming scenario RCP2.6 indicates a reduction in the potential niche by 30%, while RCP8.5 so indicates a reduction of almost 60%.

MAIN CONCLUSIONS

This research can provide information for increasing the protection of the juniper forest and for counteracting the phenomenon of local extinctions caused by anthropic pressure and climate changes.

Trade-Off and Projecting Effects of Land Use Change on Ecosystem Services under Different Policies Scenarios: A Case Study in Central China

Predicting the spatio-temporal evolution characteristics and trade-off/synergy relationships of ecosystem service value (ESV) under different policy scenarios is of great significance for realizing regional sustainable development. This study established a framework and used the geographical simulation and optimization systems-future land use simulation (GeoSOS-FLUS) model and bivariate local autocorrelation analysis to stimulate and predict the impact of land use change on the ESV of Anyang City from 1995 to 2025. We also explored the trade-offs and synergy among ecosystem services under three policy scenarios (natural evolution, cultivated land protection, and ecological protection) in 2025. Results show that (1) the land use change in Anyang from 1995 to 2025 was significant, and the degree of land use change under the cultivated land and ecological protection scenarios was more moderate than that under the natural evolution scenario; (2) The total ESV decreased between 1995 and 2015, amounting to losses of 1126 million yuan, and the decline from 2015 to 2025 under the natural evolution scenario was more significant than those under the cultivated land protection and ecological protection scenarios; and (3) an obvious synergy was observed between various ecosystem services in Anyang City under different scenarios in 2025, and the most significant synergy was observed under the natural evolution scenario. In terms of spatial distribution, the agglomeration of “high–high” synergy in the west and “low–low” synergy in the central region was significant. Local areas showed “high–low” and “low–high” trade-off relationships scattered between their built land and woodland or cultivated land. The proposed framework can provide certain scientific support for regulating land use and ecosystem services in rapidly urbanized areas.

Feasibility of reintroducing grassland megaherbivores, the greater one-horned rhinoceros, and swamp buffalo within their historic global range

Reintroduction of endangered species is an effective and increasingly important conservation strategy once threats have been addressed. The greater one-horned rhinoceros and swamp buffalo have declined through historic hunting and habitat loss. We identify and evaluate available habitat across their historic range (India, Nepal, and Bhutan) for reintroducing viable populations. We used Species Distribution Models in Maxent to identify potential habitats and evaluated model-identified sites through field visits, interviews of wildlife managers, literature, and population-habitat viability analysis. We prioritize sites based on size, quality, protection, management effectiveness, biotic pressures, and potential of conflict with communities. Our results suggest that populations greater than 50 for rhinoceros and 100 for buffalo were less susceptible to extinction, and could withstand some poaching, especially if supplemented or managed as a metapopulation. We note some reluctance by managers to reintroduce rhinoceros due to high costs associated with subsequent protection. Our analysis subsequently prioritised Corbett and Valmiki, for rhino reintroduction and transboundary complexes of Chitwan-Parsa-Valmiki and Dudhwa-Pilibhit-Shuklaphanta-Bardia for buffalo reintroductions. Establishing new safety-nets and supplementing existing populations of these megaherbivores would ensure their continued survival and harness their beneficial effect on ecosystems and conspecifics like pygmy hog, hispid hare, swamp deer, hog deer, and Bengal florican.

Functionalized microchannels as xylem-mimicking environment: Quantifying X. fastidiosa cell adhesion

Microchannels can be used to simulate xylem vessels and investigate phytopathogen colonization under controlled conditions. In this work, we explore surface functionalization strategies for polydimethylsiloxane and glass microchannels to study microenvironment colonization by Xylella fastidiosa subsp. pauca cells. We closely monitored cell initial adhesion, growth, and motility inside microfluidic channels as a function of chemical environments that mimic those found in xylem vessels. Carboxymethylcellulose (CMC), a synthetic cellulose, and an adhesin that is overexpressed during early stages of X. fastidiosa biofilm formation, XadA1 protein, were immobilized on the device's internal surfaces. This latter protocol increased bacterial density as compared with CMC. We quantitatively evaluated the different X. fastidiosa attachment affinities to each type of microchannel surface using a mathematical model and experimental observations acquired under constant flow of culture medium. We thus estimate that bacterial cells present ∼4 and 82% better adhesion rates in CMC- and XadA1-functionalized channels, respectively. Furthermore, variable flow experiments show that bacterial adhesion forces against shear stresses approximately doubled in value for the XadA1-functionalized microchannel as compared with the polydimethylsiloxane and glass pristine channels. These results show the viability of functionalized microchannels to mimic xylem vessels and corroborate the important role of chemical environments, and particularly XadA1 adhesin, for early stages of X. fastidiosa biofilm formation, as well as adhesivity modulation along the pathogen life cycle.

The Modeling and Forecasting of Carabid Beetle Distribution in Northwestern China

Simple Summary

The relationship between species and environment are an important basis for the study of biodiversity. Most researchers have found the distribution of indicator insects such as carabid beetle at the local community scale; however, a few studies on the distribution of indicator insects in grassland in China. Here, we used Generalized Additive Models (GAM) to predict temperate steppe of northwestern China carabid beetle species richness distribution, and to determine the possible underlying causal factors. Predicted values of beetle richness ranged from 3 to 12. The diversity hotspots are located in the southwest, south and southeast of the study area which have moist environment, the carabid beetle is mainly influenced by temperature and precipitation. The results underline the importance of management and conservation strategies for grassland and also provides evidence for assessing beetle diversity in temperature steppe.

Abstract

Beetles are key insect species in global biodiversity and play a significant role in steppe ecosystems. In the temperate steppe of China, the increasing degeneration of the grasslands threatens beetle species and their habitat. Using Generalized Additive Models (GAMs), we aimed to predict and map beetle richness patterns within the temperate steppe of Ningxia (China). We tested 19 environmental predictors including climate, topography, soil moisture and space as well as vegetation. Climatic variables (temperature, precipitation, soil temperature) consistently appeared among the most important predictors for beetle groups modeled. GAM generated predictive cartography for the study area. Our models explained a significant percentage of the variation in carabid beetle richness (79.8%), carabid beetle richness distribution seems to be mainly influenced by temperature and precipitation. The results have important implications for management and conservation strategies and also provides evidence for assessing and making predictions of beetle diversity across the steppe.

Introduced ant species occupy empty climatic niches in Europe

Exploring shifts in the climatic niches of introduced species can provide significant insight into the mechanisms underlying the invasion process and the associated impacts on biodiversity. We aim to test the phylogenetic signal hypothesis in native and introduced species in Europe by examining climatic niche similarity. We examined data from 134 ant species commonly found in western Europe; 130 were native species, and 4 were introduced species. We characterized their distribution patterns using species records from different databases, determined their phylogenetic relatedness, and tested for a phylogenetic signal in their optimal climatic niches. We then compared the introduced species' climatic niches in Europe with their climatic niches in their native ranges and with the climatic niches of their closest relative species in Europe. We found a strong phylogenetic signal in the optimal climatic niches of the most common ant species in Europe; however, this signal was weak for the main climatic variables that affect the distributions of introduced versus native species. Also, introduced species occupied different climatic niches in Europe than in their native ranges; furthermore, their European climatic niches did not resemble those of their closest relative species in Europe. We further discovered that there was not much concordance between the climatic niches of introduced species in their native ranges and climatic conditions in Europe. Our findings suggest that phylogenetics do indeed constrain shifts in the climatic niches of native European ant species. However, introduced species would not face such constraints and seemed to occupy relatively empty climatic niches.

Mapping the Establishment and Invasiveness Potential of Rainbow Trout (Oncorhynchus mykiss) in Turkey: With Special Emphasis on the Conservation of Native Salmonids

Rainbow trout (Oncorhynchus mykiss) has become by far the most frequently farmed freshwater fish species in Turkey, whereas very little is known about its establishment and invasiveness potential. We explored this potential through a combination of Maxent habitat suitability model and the Aquatic Species Invasiveness Screening Kit (AS-ISK) on the river basin scale by generating an overall risk score (ORS). The outcome of this approach was also incorporated with the spatial analysis of native salmonid species by generating a relative vulnerability score (RVS) to prioritize susceptibility of native species (or populations) and to propose risk hotspots by identifying their potential geographic overlap and interaction with O. mykiss. Results suggest that the northern basins (Eastern Black Sea, Western Black Sea and Marmara basins) are the most suitable basins for O. mykiss. According to the Basic Risk Assessment (BRA) threshold scores, O. mykiss is classified as “high risk” for 3 (12.0%) of the 25 river basins screened (Western Black Sea, Eastern Black Sea and Maritza-Ergene), and as “medium risk” for the remaining basins. The climate change assessment (CCA) scores negatively contributed the overall invasiveness potential of O. mykiss in 22 (88.0%) of the river basins and resulted in zero contribution for the remaining three, namely Aras-Kura, Çoruh river and Eastern Black Sea. The ORS score of river basins was lowest for Orontes and highest for Western Black Sea, whereas it was lowest for Konya-closed basin and highest for Eastern Black Sea, when CCA was associated. The micro-basins occupied by Salmo rizeensis had the highest mean habitat suitability with O. mykiss. Among the all species, S. abanticus had the highest RVS, followed by S. munzuricus and S. euphrataeus. The overall outcome of the present study also suggests that the establishment and invasiveness potential of O. mykiss may decrease under future (climate warmer) in Turkey, except for the northeast region. This study can provide environmental managers and policy makers an insight into using multiple tools for decision-making. The proposed RVS can also be considered as a complementary tool to improve IUCN red list assessment protocols of species.

The spreading of the invasive sacred ibis in Italy

The spreading of invasive species in new continents can vary from slow and limited diffusion to fast colonisations over vast new areas. We studied the sacred ibis Threskiornis aethiopicus along a 31-year period, from 1989 to 2019, with particular attention to the first area of release in NW Italy. We collected data on species distribution through observations by citizen science projects, population density by transects with distance method, breeding censuses at colonies, and post breeding censuses at roosts. The birds counted at winter roosts in NW Italy increased from a few tens up to 10,880 individuals in 2019. Sacred ibises started breeding in 1989, with a single nest in north-western Italy. The number of breeders remained very low until 2006, when both overwintering and breeding sacred ibises started to increase exponentially and expand their range throughout northern Italy with isolated breeding cases in central Italy. In 2019, the number of nests had increased to 1249 nests in 31 colonies. In NW Italy, the density of foraging birds averaged 3.9 ind./km² in winter and 1.5 ind./km² in the breeding period, with a mean size of the foraging groups of 8.9 and 2.1 birds respectively. Direct field observations and species distribution models (SDM) showed that foraging habitats were mainly rice fields and wetlands. A SDM applied to the whole Italian peninsula plus Sardinia and Sicily showed that the variables best related to the SDM were land class (rice fields and wetlands), altitude, and the temperature seasonality. The areas favourable for species expansion encompass all the plains of Northern Italy, and several areas of Tuscany, Latium, Sardinia, and Apulia.

A Bioclimate-Based Maximum Entropy Model for Comperiella calauanica Barrion, Almarinez and Amalin (Hymenoptera: Encyrtidae) in the Philippines

Simple Summary

The discovery of Comperiella calauanica a parasitoid confirmed to be the major natural enemy of the invasive diaspidid, Aspidiotus rigidus, has led to the promise of biological control in sustainable pest management of this devastating coconut pest. In this study, we employed Maximum Entropy (Maxent) to develop a bioclimate-based species distribution model (SDM) for the parasitoid from presence-only data recorded from field surveys conducted in select points the Philippines. Results of assessment of the generated model point to its excellent power in predicting either suitability of habitat, or potential occurrence or distribution of C. calauanica. Since the parasitoid is highly host-specific, the model may also apply to A. rigidus. Field surveys in select areas in the Philippines confirmed the occurrence of the invasive coconut scale in areas predicted by the model as having considerable probability of occurrence, or habitat suitability. Our findings strongly suggest the potential utility of Maxent SDMs as tools for pest invasion forecasting and GIS-aided surveillance for integrated pest management (IPM).

Abstract

Comperiella calauanica is a host-specific endoparasitoid and effective biological control agent of the diaspidid Aspidiotus rigidus, whose outbreak from 2010 to 2015 severely threatened the coconut industry in the Philippines. Using the maximum entropy (Maxent) algorithm, we developed a species distribution model (SDM) for C. calauanica based on 19 bioclimatic variables, using occurrence data obtained mostly from field surveys conducted in A. rigidus-infested areas in Luzon Island from 2014 to 2016. The calculated the area under the ROC curve (AUC) values for the model were very high (0.966, standard deviation = 0.005), indicating the model’s high predictive power. Precipitation seasonality was found to have the highest relative contribution to model development. Response curves produced by Maxent suggested the positive influence of mean temperature of the driest quarter, and negative influence of precipitation of the driest and coldest quarters on habitat suitability. Given that C. calauanica has been found to always occur with A. rigidus in Luzon Island due to high host-specificity, the SDM for the parasitoid may also be considered and used as a predictive model for its host. This was confirmed through field surveys conducted between late 2016 and early 2018, which found and confirmed the occurrence of A. rigidus in three areas predicted by the SDM to have moderate to high habitat suitability or probability of occurrence of C. calauanica: Zamboanga City in Mindanao; Isabela City in Basilan Island; and Tablas Island in Romblon. This validation in the field demonstrated the utility of the bioclimate-based SDM for C. calauanica in predicting habitat suitability or probability of occurrence of A. rigidus in the Philippines.

Planning priority conservation areas for biodiversity under climate change in topographically complex areas: A case study in Sichuan province, China

Identifying priority conservation areas plays a significant role in conserving biodiversity under climate change, but uncertainties create challenges for conservation planning. To reduce uncertainties in the conservation planning framework, we developed an adaptation index to assess the effect of topographic complexity on species adaptation to climate change, which was incorporated into the conservation framework as conservation costs. Meanwhile, the species distributions were predicted by the Maxent model, and the priority conservation areas were optimized during different periods in Sichuan province by the Marxan model. Our results showed that the effect of topographic complexity was critical for species adaptation, but the adaptation index decreased with the temperature increase. Based on the conservation targets and costs, the distributions of priority conservation areas were mainly concentrated in mountainous areas around the Sichuan Basin where may be robust to the adaptation to climate change. In the future, the distributions of priority conservation areas had no evident changes, accounting for about 26% and 28% of the study areas. Moreover, most species habitats could be conserved in terms of conservation targets in these priority conservation areas. Therefore, our approach could achieve biodiversity conservation goals and be highly practical. More importantly, quantifying the effect of topography also is critical for options for planning conservation areas in response to climate change.

The Relict Ecosystem of Maytenus senegalensis subsp. europaea in an Agricultural Landscape: Past, Present and Future Scenarios

Maytenus senegalensis subsp. europaea is a shrub belonging to the Celastraceae family, whose only European populations are distributed discontinuously along the south-eastern coast of the Iberian Peninsula, forming plant communities with great ecological value, unique in Europe. As it is an endangered species that makes up plant communities with great palaeoecological significance, the development of species distribution models is of major interest under different climatic scenarios, past, present and future, based on the fact that the climate could play a relevant role in the distribution of this species, as well as in the conformation of the communities in which it is integrated. Palaeoecological models were generated for the Maximum Interglacial, Last Maximum Glacial and Middle Holocene periods. The results obtained showed that the widest distribution of this species, and the maximum suitability of its habitat, occurred during the Last Glacial Maximum, when the temperatures of the peninsular southeast were not as contrasting as those of the rest of the European continent and were favored by higher rainfall. Under these conditions, large territories could act as shelters during the glacial period, a hypothesis reflected in the model’s results for this period, which exhibit a further expansion of M. europaea’s ecological niche. The future projection of models in around 2070, for four Representative Concentration Pathways according to the fifth report of the Intergovernmental Panel on Climate Change, showed that the most favorable areas for this species would be Campo de Dalías (southern portion of Almería province) as it presents the bioclimatic characteristics of greater adjustment to M. europaea’s ecological niche model. Currently, some of the largest specimens of the species survive in the agricultural landscapes in the southern Spain. These areas are almost totally destroyed and heavily altered by intensive agriculture greenhouses, also causing a severe fragmentation of the habitat, which implies a prospective extinction scenario in the near future.