Maximum Entropy Modeling to Predict the Impact of Climate Change on Pine Wilt Disease in China

Ganoderma lucidum: Insights on host range, diagnosis, and management strategies

Forest ecosystems play an important role in upholding life on our planet. However, the onslaught of fungal pathogens like Ganoderma lucidum, poses a threat by decimating numerous tree species. G. lucidum identified as a root pathogen, causing root rot in numerous tree species of horticulture and forestry importance. The fungus initiates infection through basidiospores, which germinate and penetrate within roots and start to degrade lignocellulosic components of plant cells. Early-stage detection of G. lucidum, is challenging, while in advance stages, the wood undergoes softening and a loss of tensile strength, rendering the disease incurable. Hence, effective management of G. lucidum necessitates a pivotal role of disease diagnostic techniques, which are currently underutilized or inadequately accessible. Subsequent implementation of suitable control measures becomes imperative to thwart disease occurrence and mitigate its impact in early stages, thus preserving the vitality of forest ecosystems. This study provides comprehensive overview of G. lucidum, covering taxonomy, pathogenicity, disease cycle, diagnosis and effective control measures, which will be helpful in formulating effective diagnostic techniques for early management of root rot disease.

Impacts of Climate Change on the Distribution of Suitable Habitats and Ecological Niche for Trollius Wildflowers in Ili River Valley, Tacheng, Altay Prefecture

Xinjiang in China is distinguished by its distinctive regional landscape and high ecological sensitivity. Trollius wildflowers represent a unique and iconic element of the mountain flower landscape in Xinjiang. However, their populations are predominantly distributed in mountainous areas, making them susceptible to climate change. Despite this, the impacts of climate change on the distribution of suitable habitats and ecological niche differentiation for Trollius wildflowers have rarely been quantified. Consequently, simulations were conducted using the R-optimized MaxEnt model to predict the suitable habitat distribution of Trollius wildflowers. This was based on the occurrence data and environmental variables for the four species of Trollius (T. altaicus, T. asiaticus, T. dschungaricus, and T. lilacinus) that exist in the study area. The simulation was conducted over a period of time, beginning with the past glacial period and extending to the present, and then to the future (2050s, 2070s, and 2090s) under multiple scenarios (SSP1-2.6, SSP3-7.0, SSP5-8.5). The simulation of suitable habitats enabled the measurement of the ecological niche breadth and differentiation. The results demonstrate that the model predictions are precisely accurate, with AUC values exceeding 0.9. Annual mean temperature (Bio1), isothermality (Bio3), and precipitation in the warmest quarter (Bio18) are the dominant climate variables, in addition to vegetation, elevation, and soil factors. The proportion of suitable habitats for Trollius wildflowers varies considerably over time, from 0.14% to 70.97%. The majority of habitat loss or gain occurs at the edges of mountains, while stable habitats are concentrated in the core of the mountains. The gravity center of suitable habitats also shifts with spatial transfer, with the shifts mainly occurring in a northeasterly-southwesterly direction. The SSP1-2.6 scenario results in the sustained maintenance of habitats, whereas the SSP3-7.0 and SSP5-8.5 scenarios present challenges to the conservation of habitats. The threshold of ecological niche breadth for Trollius wildflowers is subject to fluctuations, while the ecological niche differentiation also varies. The study aims to examine the evolution of the habitat and ecological niche of Trollius wildflowers in Xinjiang under climate change. The findings will provide theoretical support for delineating the conservation area, clarify the scope of mountain flower tourism development and protection of mountain flower resources, and promote the sustainable development of ecotourism and effective utilization of territorial space in Xinjiang.

Predicting the potential distribution of Dendrolimus punctatus and its host Pinus massoniana in China under climate change conditions

Introduction

Dendrolimus punctatus, a major pest endemic to the native Pinus massoniana forests in China, displays major outbreak characteristics and causes severe destructiveness. In the context of global climate change, this study aims to investigate the effects of climatic variations on the distribution of D. punctatus and its host, P. massoniana.

Methods

We predict their potential suitable distribution areas in the future, thereby offering a theoretical basis for monitoring and controlling D. punctatus, as well as conserving P. massoniana forest resources. By utilizing existing distribution data on D. punctatus and P. massoniana, coupled with relevant climatic variables, this study employs an optimized maximum entropy (MaxEnt) model for predictions. With feature combinations set as linear and product (LP) and the regularization multiplier at 0.1, the model strikes an optimal balance between complexity and accuracy.

Results

The results indicate that the primary climatic factors influencing the distribution of D. punctatus and P. massoniana include the minimum temperature of the coldest month, annual temperature range, and annual precipitation. Under the influence of climate change, the distribution areas of P. massoniana and its pests exhibit a high degree of similarity, primarily concentrated in the region south of the Qinling-Huaihe line in China. In various climate scenarios, the suitable habitat areas for these two species may expand to varying degrees, exhibiting a tendency to shift toward higher latitude regions. Particularly under the high emission scenario (SSP5-8.5), D. punctatus is projected to expand northwards at the fastest rate.

Discussion

By 2050, its migration direction is expected to closely align with that of P. massoniana, indicating that the pine forests will continue to be affected by the pest. These findings provide crucial empirical references for region-specific prevention of D. punctatus infestations and for the rational utilization and management of P. massoniana resources.

Predicting the geographical distribution and niche characteristics of Cotoneaster multiflorus based on future climate change

Introduction

Arid and semi-arid regions are climate-sensitive areas, which account for about 40% of the world's land surface area. Future environment change will impact the environment of these area, resulting in a sharp expansion of arid and semi-arid regions. Cotoneaster multiflorus is a multi-functional tree species with extreme cold, drought and barren resistance, as well as ornamental and medicinal functions. It was found to be one of the most important tree species for ecological restoration in arid and semi-arid areas. However, bioclimatic factors play an important role in the growth, development and distribution of plants. Therefore, exploring the response pattern and ecological adaptability of C. multiflorus to future climate change is important for the long-term ecological restoration of C. multiflorus in arid and semi-arid areas.

Methods

In this study, we predicted the potential distribution of C. multiflorus in China under different climate scenarios based on the MaxEnt 2.0 model, and discussed its adaptability and the major factors affecting its geographical distribution.

Results

The major factors that explained the geographical distribution of C. multiflorus were Annual precipitation (Bio12), Min air temperature of the coldest month (Bio6), and Mean air temperature of the coldest quarter (Bio11). However, C. multiflorus could thrive in environments where Annual precipitation (Bio12) >150 mm, Min air temperature of the coldest month (Bio6) > -42.5°C, and Mean air temperature of the coldest quarter (Bio11) > -20°C, showcasing its characteristics of cold and drought tolerance. Under different future climate scenarios, the total suitable area for C. multiflorus ranged from 411.199×104 km² to 470.191×104 km², which was 0.8~6.14 percentage points higher than the current total suitable area. Additionally, it would further shift towards higher latitude.

Discussion

The MaxEnt 2.0 model predicted the potential distribution pattern of C. multiflorus in the context of future climate change, and identified its ecological adaptability and the main climatic factors affecting its distribution. This study provides an important theoretical basis for natural vegetation restoration in arid and semi-arid areas.

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.

Potential habitat areas and priority protected areas of Tilia amurensis Rupr in China under the context of climate change

Introduction

Tilia amurensis Rupr (T. amurensis) is one endangered and national class II key protected wild plant in China. It has ornamental, material, economic, edible and medicinal values. At present, the resources of T. amurensis are decreasing, and the prediction of the distribution of its potential habitat in China can provide a theoretical basis for the cultivation and rational management of this species.

Methods

In this study, the R language was used to evaluate 358 distribution records and 38 environment variables. The MaxEnt model was used to predict the potential distribution areas of T. amurensis under the current and future climate scenarios. The dominant environmental factors affecting the distribution of T. amurensis were analyzed and the Marxan model was used to plan the priority protected areas of this species.

Results

The results showed that Bio18, Slope, Elev, Bio1, Bio9 and Bio2 were the dominant environmental factors affecting the distribution of T. amurensis. Under the future climatic scenarios, the potential suitable areas for T. amurensis will mainly distribute in the Northeast China, the total suitable area will reduce compared with the current climate scenarios, and the general trend of the centroid of suitable habitat will be towards higher latitudes. The SPF value of the best plan obtained from the priority conservation area planning was 1.1, the BLM value was 127,616, and the priority conservation area was about 57.61×104 km2. The results suggested that climate, soil and topographic factors jointly affected the potential geographical distribution of T. amurensis, and climate and topographic factors had greater influence than soil factors.

Discussion

The total suitable area of T. amurensis in China under different climate scenarios in the future will decrease, so more effective protection should be actively adopted.

Multi-scale habitat modeling framework for predicting the potential distribution of sheep gastrointestinal nematodes across Iran's three distinct climatic zones: a MaxEnt machine-learning algorithm

Ecological niche models (ENMs) serve as valuable tools in assessing the potential species distribution, identifying crucial habitat components for species associations, and facilitating conservation efforts. The current study aimed to investigate the gastrointestinal nematodes (GINs) infection in sheep, predict and analyze their ecological niches and ranges, and identify the key bioclimatic variables influencing their distribution across three distinct climatic regions in Iran. In a cross-sectional study, a total of 2140 fecal samples were collected from semi-arid (n = 800), arid (n = 500), and humid-subtropical (n = 840) climates in East Azerbaijan, Kerman, and Guilan provinces, respectively. The flotation method was employed to assess stool samples, whereby the fecal egg count (the number of parasite eggs per gram [EPG]) was ascertained for each individual specimen. Employing a presence-only approach, the multi-scale maximum entropy (MaxEnt) method was used to model GINs' habitat suitability using 93 selected points/locations. The findings revealed that Guilan (34.2%) and East Azerbaijan (19.62%) exhibited the utmost proportion of Strongyle-type eggs. East Azerbaijan province also displayed the highest proportion of Marshallagia and Nematodirus, respectively (approximately 40% and 27%), followed by Guilan and Kerman provinces, while Kerman province had the highest proportion of Trichuris (approximately 15%). Ecological niche modeling revealed that the precipitation of the driest quarter (Bio17) exerted the most significant influence on Marshallagia, Nematodirus, Trichuris, and ُSُُُtrongyle-type eggs' presence in East Azerbaijan and Kerman provinces. For Guilan province, the most influential factor defining habitat suitability for Strongyle-type eggs, Marshallagia, and Nematodirus was increasing slope. Additionally, the distribution of Trichuris was most affected by the variable Bio2 in Guilan province. The study highlights the response of GINs to climate drivers in highly suitable regions, providing insights into ecologically favorable areas for GINs. In conclusion, this study provides a better understanding of GINs and the environmental factors influencing their transmission dynamics.

Environmental DNA and remote sensing datasets reveal the spatial distribution of aquatic insects in a disturbed subtropical river system

Biodiversity datasets with high spatial resolution are critical prerequisites for river protection and management decision-making. However, traditional morphological biomonitoring is inefficient and only provides several site estimates, and there is an urgent need for new approaches to predict biodiversity on fine spatial scales throughout the entire river systems. Here, we combined the environmental DNA (eDNA) and remote sensing (RS) technologies to develop a novel approach for predicting the spatial distribution of aquatic insects with high spatial resolution in a disturbed subtropical Dongjiang River system of southeast China. First, we screened thirteen RS-based vegetation indices that significantly correlated with the eDNA-inferred richness of aquatic insects. In particular, the green normalized difference vegetation index (GNDVI) and normalized difference red-edge2 (NDRE2) were closely related to eDNA-inferred richness. Second, using the gradient boosting decision tree, our data showed that the spatial pattern of eDNA-inferred richness could achieve a high spatial resolution to 500 m reach and accurate prediction of more than 80%, and the prediction efficiency of the headwater streams (Strahler stream order = 1) was slightly higher than the downstream (Strahler stream order >1). Third, using the random forest algorithm, the spatial distribution of aquatic insects could reach a prediction rate of over 70% for the presence or absence of specific genera. Overall, this study provides a new approach to achieving high spatial resolution prediction of the distribution of aquatic insects, which supports decision-making on river diversity protection under climate changes and human impacts.

Risk assessment framework for pine wilt disease: Estimating the introduction pathways and multispecies interactions among the pine wood nematode, its insect vectors, and hosts in China

Pine wilt disease (PWD), caused by the pine wood nematode (PWN, Bursaphelenchus xylophilus), a destructive, invasive forest pathogen, poses a serious threat to global pine forest ecosystems. The global invasion of PWN has been described based on three successive phases, introduction, establishment, and dispersal. Risk assessments of the three successive PWN invasion phases can assist in targeted management efforts. Here, we present a risk assessment framework to evaluate the introduction, establishment, and dispersal risks of PWD in China using network analysis, species distribution models, and niche concepts. We found that >88 % of PWN inspection records were from the United States, South Korea, Japan, Germany, and Mexico, and 94 % of interception records were primarily from the Jiangsu, Shanghai, Shandong, Tianjin, and Zhejiang ports. Based on the nearly current climate, the areas of PWN overlap with its host Pinus species were primarily distributed in southern, eastern, Yangtze River Basin, central, and northeastern China regions. Areas of PWN overlap with its insect vector Monochamus alternatus were primarily distributed in southern, eastern, Yangtze River Basin, central, and northeastern China regions, and those of PWN overlap with the insect vector Monochamus saltuarius were primarily distributed in eastern and northeastern China. The niche between PWN and the insect vector M. alternatus was the most similar (0.68), followed by that between PWN and the insect vector M. saltuarius (0.47). Climate change will increase the suitable probabilities of PWN and its two insect vectors occurring at high latitudes, further increasing their threat to hosts in northeastern China. This risk assessment framework for PWD could be influential in preventing the entry of the PWN and mitigating their establishment and dispersal risks in China. Our study provides substantial clues for developing a framework to improve the risk assessment and surveillance of biological invasions worldwide.

The predicted potential distribution of Aedes albopictus in China under the shared socioeconomic pathway (SSP)1-2.6

Aedes albopictus (Skuse) (Diptera: Culicidae) is one of the 100 most invasive species in the world and represents a significant threat to public health. The distribution of Ae. albopictus has been expanding rapidly due to increased international trade, population movement, global warming and accelerated urbanization. Consequently, it is very important to know the potential distribution area of Ae. albopictus in advance for early warning and control of its spread and invasion. We randomly selected 282 distribution sites from 27 provincial-level administrative regions in China, and used the GARP and MaxEnt models to analyze and predict the current and future distribution areas of Ae. albopictus in China. The results showed that the current range of Ae. albopictus in China covers most provinces such as Yunnan and Guizhou Provinces, and the distribution of Ae. albopictus in border provinces such as Tibet, Gansu and Jilin Provinces tend to expand westwards. In addition, the potential distribution area of Ae. albopictus in China will continue to expand westwards due to future climate change under the SSP126 climate scenario. Furthermore, the results of environmental factor filtering showed that temperature and precipitation had a large effect on the distribution probability of Ae. albopictus.

Building Resilient communities: Techno-economic assessment of standalone off-grid PV powered net zero energy (NZE) villages

The use of renewable energy resources for off-grid electricity production has gained more importance in recent decades for meeting the energy needs of remote areas, even with limited resources. This research aims to provide an optimized and cost-effective approach for generating electricity in rural areas. By using current methodology, a stand alone energy source of PV is designed for development of NZE village. Solar irradiance of the selected location is 6.16 kWh/m2/day while the estimated electric load data for whole village is 64.259 kWh. Electric load and solar irradiance of the loaction is used in the Hybrid Optimization Model for Electric Renewable (HOMER) to design and analyze the techno-economic feasibility of the stand alone PV system to meet the load requirements. The study obtained the total Net Present Cost (NPC) of $0.511 M and the Cost Of Electricity (COE) is 2.26$/unit through the HOMER analysis, which is further refined by performing sensitivity analysis using parameters such as PV panel price, battery price, solar irradiance, variations in electric load and discount rates. According to the results, system is feasibile by annual electricity production of 30,078 kWh with initial capital investment of $0.434 M. This analysis compared the system performance and showed that it is economically and technically viable to meet the complete electricity needs of the village with a payback period of 7.2 years. Research can be utilized for policy making and implementation of NZE approach in remote areas by the government.

Impact of climate change on the geographical distribution and niche dynamics of Gastrodia elata

Background

Gastrodia elata is widely used in China as a valuable herbal medicine. Owing to its high medicinal and nutrient value, wild resources of G. elata have been overexploited and its native areas have been severely damaged. Understanding the impacts of climate change on the distribution of this endangered species is important for the conservation and sustainable use of G. elata.

Methods

We used the optimized maximum entropy model to simulate the potential distribution of G. elata under contemporary and future time periods (1970-2000, 2050s, 2070s, and 2090s) and different climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Under these conditions, we investigated the key environmental factors influencing the distribution of G. elata as well as the spatial and temporal characteristics of its niche dynamics.

Results

With high Maxent model accuracy (AUCmean = 0.947 ± 0.012, and the Kappa value is 0.817), our analysis revealed that annual precipitation, altitude, and mean temperature of driest quarter are the most important environmental factors influencing the distribution of G. elata. Under current bioclimatic conditions, the potentially suitable area for G. elata in China is 71.98 × 10⁴ km², while the highly suitable region for G. elata growth is 7.28 × 10⁴ km². Our models for three future periods under four climate change scenarios indicate that G. elata can maintain stable distributions in southern Shaanxi, southwestern Hubei, and around the Sichuan basin, as these areas are highly suitable for its growth. However, the center of the highly suitable areas of G. elata shift depending on different climatic scenarios. The values of niche overlap for G. elata show a decreasing trend over the forecasted periods, of which the niche overlap under the SSP3-7.0 scenario shows the greatest decrease.

Discussions

Under the condition of global climate change in the future, our study provides basic reference data for the conservation and sustainable utilization of the valuable and endangered medicinal plant G. elata. It is important to carefully choose the protection area of G. elata wild resources according the suitable area conditions modeled. Moreover, these findings will be valuable for providing insights into the breeding and artificial cultivation of this plant, including the selection of suitable areas for planting.

Prediction of the potentially suitable areas of Ligularia virgaurea and Ligularia sagitta on the Qinghai-Tibet Plateau based on future climate change using the MaxEnt model

Ligularia virgaurea and Ligularia sagitta are two species of poisonous plants with strong invasiveness in natural grasslands in China that have caused considerable harm to animal husbandry and the ecological environment. However, little is known about their suitable habitats and the key environmental factors affecting their distribution. Although some studies have reported the distributions of poisonous plants on the Qinghai-Tibet Plateau (QTP) and predicted their potential distributions at local scales in some regions under climate change, there have been few studies on the widespread distributions of L. virgaurea and L. sagitta. In this study, we recorded 276 and 118 occurrence points of L. virgaurea and L. sagitta on the QTP using GPS, and then used the MaxEnt model to predict the distribution of suitable habitats. Results showed that (1) under current climate conditions, L. virgaurea and L. sagitta are mainly distributed in southern Gansu, eastern Qinghai, northwestern Sichuan, eastern Tibet, and southwestern Yunnan, accounting for approximately 34.9% and 39.8% of the total area of the QTP, respectively; (2) the main environmental variables affecting the distribution of suitable habitats for L. virgaurea and L. sagitta are the Human Footprint Index (52.8%, 42.2%), elevation (11%, 4.4%), soil total nitrogen (18.9%, 4.2%), and precipitation seasonality (5.1%, 7.3%); and (3) in the future, in the 2050s and 2070s, the area of habitat of intermediate suitability for L. virgaurea will spread considerably in northwest Sichuan, while that of high suitability for L. sagitta will spread to eastern Tibet and western Sichuan.

Prediction of Suitable Habitat Distribution of Cryptosphaeria pullmanensis in the World and China under Climate Change

Years of outbreaks of woody canker (Cryptosphaeria pullmanensis) in the United States, Iran, and China have resulted in massive economic losses to biological forests and fruit trees. However, only limited information is available on their distribution, and their habitat requirements have not been well evaluated due to a lack of research. In recent years, scientists have utilized the MaxEnt model to estimate the effect of global temperature and specific environmental conditions on species distribution. Using occurrence and high resolution ecological data, we predicted the spatiotemporal distribution of C. pullmanensis under twelve climate change scenarios by applying the MaxEnt model. We identified climatic factors, geography, soil, and land cover that shape their distribution range and determined shifts in their habitat range. Then, we measured the suitable habitat area, the ratio of change in the area of suitable habitat, the expansion and shrinkage of maps under climate change, the direction and distance of range changes from the present to the end of the twenty-first century, and the effect of environmental variables. C. pullmanensis is mostly widespread in high-suitability regions in northwestern China, the majority of Iran, Afghanistan, and Turkey, northern Chile, southwestern Argentina, and the west coast of California in the United States. Under future climatic conditions, climate changes of varied intensities favored the expansion of suitable habitats for C. pullmanensis in China. However, appropriate land areas are diminishing globally. The trend in migration is toward latitudes and elevations that are higher. The estimated area of possible suitability shifted eastward in China. The results of the present study are valuable not only for countries such as Morocco, Spain, Chile, Turkey, Kazakhstan, etc., where the infection has not yet fully spread or been established, but also for nations where the species has been discovered. Authorities should take steps to reduce greenhouse gas emissions in order to restrict the spread of C. pullmanensis. Countries with highly appropriate locations should increase their surveillance, risk assessment, and response capabilities.

Effect of global warming on the potential distribution of a holoparasitic plant (Phelypaea tournefortii): both climate and host distribution matter

Phelypaea tournefortii (Orobanchaceae) primarily occurs in the Caucasus (Armenia, Azerbaijan, Georgia, and N Iran) and Turkey. This perennial, holoparasitic herb is achlorophyllous and possesses one of the most intense red flowers among all plants worldwide. It occurs as a parasite on the roots of several Tanacetum (Asteraceae) species and prefers steppe and semi-arid habitats. Climate change may affect holoparasites both directly through effects on their physiology and indirectly as a consequence of its effects on their host plants and habitats. In this study, we used the ecological niche modeling approach to estimate the possible effects of climate change on P. tournefortii and to evaluate the effect of its parasitic relationships with two preferred host species on the chances of survival of this species under global warming. We used four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) and three different simulations (CNRM, GISS-E2, INM). We modeled the species' current and future distribution using the maximum entropy method implemented in MaxEnt using seven bioclimatic variables and species occurrence records (Phelypaea tournefortii - 63 records, Tanacetum argyrophyllum - 40, Tanacetum chiliophyllum - 21). According to our analyses, P. tournefortii will likely contract its geographical range remarkably. In response to global warming, the coverage of the species' suitable niches will decrease by at least 34%, especially in central and southern Armenia, Nakhchivan in Azerbaijan, northern Iran, and NE Turkey. In the worst-case scenario, the species will go completely extinct. Additionally, the studied plant's hosts will lose at least 36% of currently suitable niches boosting the range contraction of P. tournefortii. The GISS-E2 scenario will be least damaging, while the CNRM will be most damaging to climate change for studied species. Our study shows the importance of including ecological data in niche models to obtain more reliable predictions of the future distribution of parasitic plants.

Effects of trunk injection with emamectin benzoate on arthropod diversity

BACKGROUND

Pine wood nematode is a major plant quarantine object in the world. Trunk injection is an effective method for controlling pests that cause disease. To evaluate the ecological safety of trunk injection with emamectin benzoates in forests of Pinus massoniana, the community diversity and community composition of soil arthropods and flying insects (Hymenoptera) were studied at different stages of trunk injection.

RESULTS

The dominant taxonomic groups of soil arthropods were Collembola (30.80%), Insecta (26.42%), and Arachnida (23.84%). The taxonomic groups of flying insects (Hymenoptera) were Ichneumonidae (48.94%), Formicidae (14.10%), and Braconidae (8.44%). Trunk injection with emamectin benzoate has no significant effect on the community diversity indices of total soil arthropods and flying insects (Hymenoptera). However, it has a significant effect on the community diversity indices of detritivores for soil arthropods. It changed the community composition of soil arthropods but did not impact the community composition of flying insects (Hymenoptera). Redundancy analysis of arthropod community structure and environmental variables showed that total potassium, residual of green leaf, and residual of litter leaf have a significant impact on the community structure of soil arthropods, and total phosphorus, total nitrogen, water content, organic matter, and total potassium have a significant impact on the community structure of flying insects (Hymenoptera).

CONCLUSION

Trunk injection with emamectin benzoate is safe for the ecological environment. This study provides a new insight into the field for the prevention and control of pine wood nematode disease, which is of great significance to forest management and pest control. © 2022 Society of Chemical Industry.

Priority planting area planning for cash crops under heavy metal pollution and climate change: A case study of Ligusticum chuanxiong Hort

INTRODUCTION

Soil pollution by heavy metals and climate change pose substantial threats to the habitat suitability of cash crops. Discussing the suitability of cash crops in this context is necessary for the conservation and management of species. We developed a comprehensive evaluation system that is universally applicable to all plants stressed by heavy metal pollution.

METHODS

The MaxEnt model was used to simulate the spatial distribution of Ligusticum chuanxiong Hort within the study area (Sichuan, Shaanxi, and Chongqing) based on current and future climate conditions (RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios). We established the current Cd pollution status in the study area using kriging interpolation and kernel density. Additionally, the three scenarios were used in prediction models to simulate future Cd pollution conditions based on current Cd pollution data. The current and future priority planting areas for L. chuanxiong were determined by overlay analysis, and two levels of results were obtained.

RESULTS

The results revealed that the current first- and secondary-priority planting areas for L. chuanxiong were 2.06 ×10³ km² and 1.64 ×10⁴ km², respectively. Of these areas, the seven primary and twelve secondary counties for current L. chuanxiong cultivation should be given higher priority; these areas include Meishan, Qionglai, Pujiang, and other regions. Furthermore, all the priority zones based on the current and future scenarios were mainly concentrated on the Chengdu Plain, southeastern Sichuan and northern Chongqing. Future planning results indicated that Renshou, Pingwu, Meishan, Qionglai, Pengshan, and other regions are very important for L. chuanxiong planting, and a pessimistic scenario will negatively impact this potential planting. The spatial dynamics of priority areas in 2050 and 2070 clearly fluctuated under different prediction scenarios and were mainly distributed in northern Sichuan and western Chongqing.

DISCUSSION

Given these results, taking reasonable measures to replan and manage these areas is necessary. This study provides. not only a useful reference for the protection and cultivation of L. chuanxiong, but also a framework for analyzing other cash crops.

MaxEnt modeling to show patterns of coastal habitats of reef-associated fish in the South and East China Seas

Reef-associated fish are a crucial source of protein for coastal residents and play an important role in the economy and ecology of marine ecosystems. However, human activities and climate change have led to the degradation of their habitats in the South China Sea (SCS) and East China Sea (ECS). This study models the potential habitats of reef-associated fish in the SCS and ECS between 1993 and 2019 using high-spatial-resolution environmental factors and fish presence data, estimates the importance of environmental factors on habitat distribution and identifies seasonal variation and distribution shifts over recent decades, the results show moderate and highly suitable areas for reef-associated fish in the region total 360,000 km2. Sea body temperature, chlorophyll-α concentration, and seawater salinity are crucial for determining the distribution of reef-associated fish. Moreover, reef-associated fish are also sensitive to seawater temperature in winter. Suitable areas for reef-associated fish near coastlines have decreased due to environmental changes within the region. The findings of this study offer valuable resource for developing fishery management and conservation strategies for this important functional group.

Discovery of entomopathogenic fungi across geographical regions in southern China on pine sawyer beetle Monochamus alternatus and implication for multi-pathogen vectoring potential of this beetle

Entomopathogen-based biocontrol is crucial for blocking the transmission of vector-borne diseases; however, few cross-latitudinal investigations of entomopathogens have been reported for vectors transmitting woody plant diseases in forest ecosystems. The pine sawyer beetle Monochamus alternatus is an important wood borer and a major vector transmitting pine wilt disease, facilitating invasion of the pinewood nematode Bursaphelenchus xylophilus (PWN) in China. Due to the limited geographical breadth of sampling regions, species diversity of fungal associates (especially entomopathogenic fungi) on M. alternatus adults and their potential ecological functions have been markedly underestimated. In this study, through traditional fungal isolation with morphological and molecular identification, 640 fungal strains (affiliated with 15 genera and 39 species) were isolated from 81 beetle cadavers covered by mycelia or those symptomatically alive across five regional populations of this pest in southern China. Multivariate analyses revealed significant differences in the fungal community composition among geographical populations of M. alternatus, presenting regionalized characteristics, whereas no significant differences were found in fungal composition between beetle genders or among body positions. Four region-representative fungi, namely, Lecanicillium attenuatum (Zhejiang), Aspergillus austwickii (Sichuan), Scopulariopsis alboflavescens (Fujian), and A. ruber (Guangxi), as well as the three fungal species Beauveria bassiana, Penicillium citrinum, and Trichoderma dorotheae, showed significantly stronger entomopathogenic activities than other fungi. Additionally, insect-parasitic entomopathogenic fungi (A. austwickii, B. bassiana, L. attenuatum, and S. alboflavescens) exhibited less to no obvious phytopathogenic activities on the host pine Pinus massoniana, whereas P. citrinum, Purpureocillium lilacinum, and certain species of Fusarium spp.-isolated from M. alternatus body surfaces-exhibited remarkably higher phytopathogenicity. Our results provide a broader view of the entomopathogenic fungal community on the vector beetle M. alternatus, some of which are reported for the first time on Monochamus spp. in China. Moreover, this beetle might be more highly-risk in pine forests than previously considered, as a potential multi-pathogen vector of both PWN and phytopathogenic fungi.

Predicting the Potential Distribution of Pine Wilt Disease in China under Climate Change

The primary culprits of pine wilt disease (PWD), an epidemic forest disease that significantly endangers the human environment and the world's forest resources, are pinewood nematodes (PWN, Bursaphelenchus xylophilus). The MaxEnt model has been used to predict and analyze the potential geographic spread of PWD in China under the effects of climate change and can serve as a foundation for high-efficiency monitoring, supervision, and prompt prevention and management. In this work, the MaxEnt model's criteria settings were optimized using data from 646 PWD infestation sites and seven climate variables from the ENMeval data package. It simulated and forecasted how PWD may be distributed under present and future (the 2050s and 2070s) climatic circumstances, and the key climate factors influencing the disease were examined. The area under AUC (area under receiver operating characteristic (ROC) curve) is 0.940 under the parameters, demonstrating the accuracy of the simulation. Under the current climate conditions, the moderately and highly suitable habitats of PWD are distributed in Anhui, Jiangxi, Hubei, Hunan, Guangdong, Guangxi, Sichuan, and other provinces. The outcomes demonstrated that the fundamental climate variables influencing the PWD distribution were rainfall and temperature, specifically including maximum temperature of warmest month, mean temperature of driest quarter, coefficient of variation of precipitation seasonality, and precipitation of wettest quarter. The evaluation outcomes of the MaxEnt model revealed that the total and highly suitable areas of PWD will expand substantially by both 2050 and 2070, and the potential distribution of PWD will have a tendency to spread towards high altitudes and latitudes.

Climate change threatens the distribution of major woody species and ecosystem services provision in southern Africa

In southern Africa, woody vegetation provides essential ecological, regulation, and cultural ecosystem services (ES), yet many species and ecosystems are increasingly threatened by climate change and land-use transformations. We investigated the effect of climate change on the distribution of eight species in 18 countries in southern Africa, covering 36% of the continent. We proposed a loser/winner ranking of the species based on the changes in land climatic suitability within their historical distribution and future gains and losses of suitable areas. We interpreted these findings in terms of changes in key ES (timber, food, and energy) provision and identified hotspots of ES provision decline. We used species presence data from the Global Biodiversity Information Facility, climatic data from the AfriClim dataset, and the MaxEnt algorithm to project the changes in species-specific land climatic suitability. Among the eight investigated species, the baseline suitability range of Mopane (Colophosperm mopane) was least affected by climate change. At the same time, the area of its future distribution was projected to double, rendering it a regional winner. Another two species, manketti (Schinziophyton rautanenii) and leadwood (Combretum imberbe) showed high future gains too; however, the impact on their baseline suitability range differed between the climatic scenarios. The baseline range of African rosewood (Guibourtia coleosperma) declined entirely, and the future gains were negligible, rendering the species a regional loser. The effect of climate change was particularly severe on timber-producing species (four out of eight species), while species providing food (four species) and energy (four species) were affected less. Our projections portrayed distinct hotspot and coldspot areas, where climatic suitability for multiple species was concurrently projected to decline or persist. This assessment can inform spatially targeted adaptation and conservation actions and strategies, which are currently lacking in many African regions.

Predicting suitable habitats of Melia azedarach L. in China using data mining

Melia azedarach L. is an important economic tree widely distributed in tropical and subtropical regions of China and some other countries. However, it is unclear how the species’ suitable habitat will respond to future climate changes. We aimed to select the most accurate one among seven data mining models to predict the current and future suitable habitats for M. azedarach in China. These models include: maximum entropy (MaxEnt), support vector machine (SVM), generalized linear model (GLM), random forest (RF), naive bayesian model (NBM), extreme gradient boosting (XGBoost), and gradient boosting machine (GBM). A total of 906 M. azedarach locations were identified, and sixteen climate predictors were used for model building. The models’ validity was assessed using three measures (Area Under the Curves (AUC), kappa, and overall accuracy (OA)). We found that the RF provided the most outstanding performance in prediction power and generalization capacity. The top climate factors affecting the species’ suitable habitats were mean coldest month temperature (MCMT), followed by the number of frost-free days (NFFD), degree-days above 18 °C (DD > 18), temperature difference between MWMT and MCMT, or continentality (TD), mean annual precipitation (MAP), and degree-days below 18 °C (DD < 18). We projected that future suitable habitat of this species would increase under both the RCP4.5 and RCP8.5 scenarios for the 2011–2040 (2020s), 2041–2070 (2050s), and 2071–2100 (2080s). Our findings are expected to assist in better understanding the impact of climate change on the species and provide scientific basis for its planting and conservation.

Modeling current geographic distribution and future range shifts of Sanghuangporus under multiple climate change scenarios in China

The genus Sanghuangporus is well-known for its edible and medicinal values. In this study, the most comprehensive occurrence records of Sanghuangporus with accurate species identification are subjected to MaxEnt, to model the current geographic distribution and future range shifts under multiple climate change scenarios in China. The current potential distribution model of Sanghuangporus is excellently predicted as indicated by the value of Area Under Receiver Operator Characteristic Curve. The current potential distribution basically corresponds to the known occurrence records of Sanghuangporus, and provides clues to new suitable habitats. The critical environmental variables to the distribution are annual precipitation, host plant, annual mean temperature and elevation. Host plant is not the most critical contribution to the model, but it indeed plays a decisive role in restricting the distribution of Sanghuangporus. This role is further confirmed by the distribution area of the highly suitable habitat increasing by 155.468%, when excluding host plant from environmental variables. For future scenarios, generally the area of highly suitable habitat for Sanghuangporus extremely increases, but the locations do not change a lot. In conclusion, this study provides important ecological information for the utilization and conservation of the edible and medicinal fungus Sanghuangporus.

Simulation the potential distribution of Dendrolimus houi and its hosts, Pinus yunnanensis and Cryptomeria fortunei, under climate change in China

Due to climate change, it is significant to explore the impact of rising temperatures on the distribution of Dendrolimus houi Lajonquiere (Lepidoptera) and its host plants, Pinus yunnanensis and Cryptomeria fortunei, and to simulate their suitable future distribution areas in order to provide a theoretical basis for the monitoring of, and early warning about, D. houi and the formulation of effective prevention and control policies. Based on the known distribution areas of, and relevant climate data for, D. houi, P. yunnanensis, and C. fortunei, their suitable habitat in China was predicted using the ENMeval data package in order to adjust the maximum entropy (MaxEnt) model parameters. The results showed that the regularization multiplier was 0.5 when the feature combination was LQHPT, with a MaxEnt model of lowest complexity and excellent prediction accuracy. The main climate variable affecting the geographical distribution of D. houi, P. yunnanensis, and C. fortunei is temperature, specifically including isothermality, temperature seasonality, maximum temperature of warmest month, minimum temperature of warmest month, average temperature of coldest quarter. The potential suitable distribution areas for P. yunnanensis and D. houi were similar under climate change, mainly distributed in southwest China, while C. fortunei was mainly distributed in southeast China. Under different future-climate scenarios, the areas suitable for the three species will increase, except for P. yunnanensis in the 2070s under Shared Socioeconomic Pathway 5-8.5. With climate change, all three species were found to have a tendency to migrate to higher latitudes and higher altitudes. The centroids of the areas suitable for P. yunnanensis and D. houi will migrate to the northwest and the centroids of the areas suitable for C. fortunei will migrate to the northeast.

Climate Change Influences the Spread of African Swine Fever Virus

Climate change is an inevitable and urgent issue in the current world. African swine fever virus (ASFV) is a re-emerging viral animal disease. This study investigates the quantitative association between climate change and the potential spread of ASFV to a global extent. ASFV in wild boar outbreak locations recorded from 1 January 2019 to 29 July 2022 were sampled and investigated using the ecological distribution tool, the Maxent model, with WorldClim bioclimatic data as the predictor variables. The future impacts of climate change on ASFV distribution based on the model were scoped with Representative Concentration Pathways (RCP 2.6, 4.5, 6.0, and 8.5) scenarios of Coupled Model Intercomparison Project 5 (CMIP5) bioclimatic data for 2050 and 2070. The results show that precipitation of the driest month (Bio14) was the highest contributor, and annual mean temperature (Bio1) was obtained as the highest permutation importance variable on the spread of ASFV. Based on the analyzed scenarios, we found that the future climate is favourable for ASFV disease; only quantitative ratios are different and directly associated with climate change. The current study could be a reference material for wildlife health management, climate change issues, and World Health Organization sustainability goal 13: climate action.

Application of MaxEnt Modeling and HRM Analysis to Support the Conservation and Domestication of Gevuina avellana Mol. in Central Chile

The Chilean hazelnut (Gevuina avellana Mol., Proteaceae) is a native tree of Chile and Argentina of edible fruit-type nut. We applied two approaches to contribute to the development of strategies for mitigation of the effects of climate change and anthropic activities in G. avellana. It corresponds to the first report where both tools are integrated, the MaxEnt model to predict the current and future potential distribution coupled with High-Resolution Melting Analysis (HRM) to assess its genetic diversity and understand how the species would respond to these changes. Two global climate models: CNRM-CM6-1 and MIROC-ES2L for four Shared Socioeconomic Pathways: 126, 245, 370, and 585 (2021−2040; 2061−2080) were evaluated. The annual mean temperature (43.7%) and water steam (23.4%) were the key factors for the distribution current of G. avellana (AUC = 0.953). The future prediction model shows to the year 2040 those habitat range decreases at 50% (AUC = 0.918). The genetic structure was investigated in seven natural populations using eight EST-SSR markers, showing a percentage of polymorphic loci between 18.69 and 55.14% and low genetic differentiation between populations (Fst = 0.052; p < 0.001). According to the discriminant analysis of principal components (DAPC) we identified 10 genetic populations. We conclude that high-priority areas for protection correspond to Los Avellanos and Punta de Águila populations due to their greater genetic diversity and allelic richness.

Identifying the natural reserve area of Cistanche salsa under the effects of multiple host plants and climate change conditions using a maximum entropy model in Xinjiang, China

Cistanche salsa (C. A. Mey.) G. Beck, a holoparasitic desert medicine plant with multiple hosts, is regarded as a potential future desert economic plant. However, as a result of excessive exploitation and poaching, its wild resources have become scarce. Thus, before developing its desert economic value, this plant has to be protected, and the identification of its natural reserve is currently the top priority. However, in previous nature reserve prediction studies, the influence of host plants has been overlooked, particularly in holoparasitic plants with multiple hosts. In this study, we sought to identify the conservation areas of wild C. salsa by considering multiple host-plant interactions and climate change conditions using the MaxEnt model. Additionally, a Principal Component Analysis (PCA) was used to reduce the autocorrelation between environmental variables. The effects of the natural distribution of the host plants in terms of natural distribution from the perspective of niche similarities and extrapolation detection were considered by filtering the most influential hosts: Krascheninnikovia ceratoides (Linnaeus), Gueldenstaedt, and Nitraria sibirica Pall. Additionally, the change trends in these hosts based on climate change conditions combined with the change trends in C. salsa were used to identify a core protection area of 126483.5 km². In this article, we corrected and tried to avoid some of the common mistakes found in species distribution models based on the findings of previous research and fully considered the effects of host plants for multiple-host holoparasitic plants to provide a new perspective on the prediction of holoparasitic plants and to provide scientific zoning for biodiversity conservation in desert ecosystems. This research will hopefully serve as a significant reference for decision-makers.

Predicting the potential distribution of four endangered holoparasites and their primary hosts in China under climate change

Climate change affects parasitic plants and their hosts on distributions. However, little is known about how parasites and their hosts shift in distribution, and niche overlap in response to global change remains unclear to date. Here, the potential distribution and habitat suitability of four endangered holoparasites and their primary hosts in northern China were predicted using MaxEnt based on occurrence records and bioclimatic variables. The results indicated that (1) Temperature annual range (Bio7) and Precipitation of driest quarter (Bio17) were identified as the common key climatic factors influencing distribution (percentage contribution > 10%) for Cynomorium songaricum vs. Nitraria sibirica (i.e., parasite vs. host); Temperature seasonality (Bio4) and Precipitation of driest month (Bio14) for Boschniakia rossica vs. Alnus mandshurica; Bio4 for Cistanche deserticola vs. Haloxylon ammodendron; Precipitation of warmest quarter (Bio18) for Cistanche mongolica vs. Tamarix ramosissima. Accordingly, different parasite-host pairs share to varying degree the common climatic factors. (2) Currently, these holoparasites had small suitable habitats (i.e., moderately and highly) (0.97-3.77%), with few highly suitable habitats (0.19-0.81%). Under future scenarios, their suitable habitats would change to some extent; their distribution shifts fell into two categories: growing type (Boschniakia rossica and Cistanche mongolica) and fluctuating type (Cynomorium songaricum and Cistanche deserticola). In contrast, the hosts' current suitable habitats (1.42-13.43%) varied greatly, with highly restricted suitable habitats (0.18-1.00%). Under future scenarios, their suitable habitats presented different trends: growing type (Nitraria sibirica), declining type (Haloxylon ammodendron) and fluctuating type (the other hosts). (3) The niche overlaps between parasites and hosts differed significantly in the future, which can be grouped into two categories: growing type (Boschniakia rossica vs. Alnus mandshurica, Cistanche mongolica vs. Tamarix ramosissima), and fluctuating type (the others). Such niche overlap asynchronies may result in severe spatial limitations of parasites under future climate conditions. Our findings indicate that climate factors restricting parasites and hosts' distributions, niche overlaps between them, together with parasitic species identity, may jointly influence the suitable habitats of parasitic plants. Therefore, it is necessary to take into account the threatened holoparasites themselves in conjunction with their suitable habitats and the parasite-host association when developing conservation planning in the future.

Changes in Mangrove Carbon Stocks and Exposure to Sea Level Rise (SLR) under Future Climate Scenarios

Mangrove ecosystems are threatened by a variety of anthropogenic changes, including climate change. The main aim of this research is to quantify the spatial variation in the different mangrove carbon stocks, aboveground carbon (AGC), belowground carbon (BGC), and soil carbon (SOC), under future climate scenarios. Additionally, we sought to identify the magnitude of sea-level rise (SLR) exposure with the view of identifying the mangrove regions most likely to face elevated inundation. Different representative concentration pathways (RCPs) ranging from the most optimistic (RCP 2.6) to medium emissions (RCP 4.5) and the most pessimistic (RCP 8.5) were considered for 2070. We used the Marine Ecoregions of the World (MEOW), a biogeographical classification of coastal ecosystems, to quantify the variation in future carbon stocks at a regional scale and identify areas of potential carbon stock losses and gains. Here, we showed that the mangroves of Central and Western Indo-Pacific islands (Andamans, Papua New Guinea, and Vanuatu), the west African coast, and northeastern South America will be the worst hit and are projected to affect all three carbon stocks under all future scenarios. For instance, the Andaman ecoregion is projected to have an 11–25% decline in SOC accumulation, while the Western Indo-Pacific realm is projected to undergo the sharpest declines, ranging from 10% to 12% under all three scenarios. Examples of these areas are those in Amazonia and the eastern part of South Asia (such as in the Northern Bay of Bengal ecoregion). Based on these findings, conservation management of mangroves can be conducted.

MaxEnt model-based prediction of potential distributions of Parnassia wightiana (Celastraceae) in China

The maximum entropy (MaxEnt) model for predicting the potential suitable habitat of species has been commonly employed in many ecological and biological applications by using presence-only occurrence records along with associated environmental factors. Parnassiawightiana, a perennial herb, is a cold-adapted plant distributed across three diversity hotspots in China, including the Hengduan Range, Central China and the Lingnan region. The MaxEnt model was used to simulate the historic, current and future distribution trends of P.wightiana, as well as to analyse its distribution pattern in each historical period and explore the causes of species distribution changes. The results of our analysis indicated that annual precipitation, annual temperature range and mean temperature of the warmest quarter were the key bioclimatic variables affecting the distribution of P.wightiana. Most temperate species retracted into smaller refugial areas during glacial periods and experienced range expansion during interglacial periods. Possible refugia of the species were inferred to be located in the Hengduan Range and Qinling Regions.

Risk Prediction and Variable Analysis of Pine Wilt Disease by a Maximum Entropy Model

Pine wilt disease (PWD) has caused a huge damage to pine forests. PWD is mainly transmitted by jumping diffusion, affected by insect vectors and human activities. Since the results of climate change, pine wood nematode (PWN—Bursaphelenchus xylophilus) has begun invading the temperate zones and higher elevation area. In this situation, predicting the distribution of PWD is an important part of the prevention and control of the epidemic situation. The research established the Maxent model to conduct a multi-angle, fine-scale prediction on the risk distribution of PWD. We adjusted two parameters, regularization multiplier (RM) and feature combination (FC), to optimize the model. Influence factors were selected and divided into natural, landscape, and human variables, according to the physical characteristics and spread rules of PWD. The middle-suitability regions and high-suitability regions are distributed in a Y-shape, and divided the study area into three parts. The high-suitability areas are concentrated in the region with high temperature, low elevation, and intensive precipitation. Among the selected variables, natural factors still play the most important role in the distribution of the disease, and human factors and landscape factors are also worked well. The permutation importance of factors is different due to differences in climate and other conditions in different regions. The multi-angle, fine-scale model can help provide useful information for effective control and tactical management of PWD.

Spatiotemporal Dynamics and Factors Driving the Distributions of Pine Wilt Disease-Damaged Forests in China

Many forests have suffered serious economic losses and ecological consequences of pine wilt disease (PWD) outbreaks. Climate change and human activities could accelerate the distribution of PWD, causing the exponential expansion of damaged forest areas in China. However, few studies have analyzed the spatiotemporal dynamics and the factors driving the distribution of PWD-damaged forests using continuous records of long-term damage, focusing on short-term environmental factors that influence multiple PWD outbreaks. We used a maximum entropy (MaxEnt) model that incorporated annual meteorological and human activity factors, as well as temporal dependence (the PWD distribution in the previous year), to determine the contributions of environmental factors to the annual distribution of PWD-damaged forests in the period 1982–2020. Overall, the MaxEnt showed good performance in modeling the PWD-damaged forest distributions between 1982 and 2020. Our results indicate that (i) the temporal lag dependence term for the presence/absence of PWD was the best predictor of the distribution of PWD-damaged forests; and (ii) Bio14 (precipitation in the driest month) was the most important meteorological factor for affecting the PWD-damaged forests. These results are essential to understanding the factors governing the distribution of PWD-damaged forests, which is important for forest management and pest control worldwide.

The Potential of Esteya spp. for the Biocontrol of the Pinewood Nematode, Bursaphelenchus xylophilus

The pinewood nematode (PWN), Bursaphelenchus xylophilus, is the causal agent of pine wilt disease (PWD) and a quarantine organism in many countries. Managing PWD involves strict regulations and heavy contingency plans, and present climate change scenarios predict a spread of the disease. The urgent need for sustainable management strategies has led to an increasing interest in promising biocontrol agents capable of suppressing the PWN, like endoparasitic nematophagous fungi of the Esteya genus. Here, we review different aspects of the biology and ecology of these nematophagous fungi and provide future prospects.

Climate Change Increases the Expansion Risk of Helicoverpa zea in China According to Potential Geographical Distribution Estimation

Simple Summary

Helicoverpa zea is one of the most destructive lepidopteran agricultural pests in the world and can disperse long distances both with and without human transportation. It is listed in the catalog of quarantine pests for plants imported to the People’s Republic of China but has not yet been reported in China. On the basis of 1781 global distribution records of H. zea and eight bioclimatic variables, we predicted the potential geographical distributions (PGDs) of H. zea by using a calibrated MaxEnt model. The results showed that the PGDs of H. zea under the current climate are large in China. Future climate changes under shared socioeconomic pathways (SSP) 1-2.6, SSP2-4.5, and SSP5-8.5 for both the 2030s and 2050s will facilitate the expansion of PGDs for H. zea. Helicoverpa zea has a high capacity for colonization by introduced individuals in China. Customs ports should pay attention to the host plants of H. zea and containers harboring this pest.

Abstract

Helicoverpa zea, a well-documented and endemic pest throughout most of the Americas, affecting more than 100 species of host plants. It is a quarantine pest according to the Asia and Pacific Plant Protection Commission (APPPC) and the catalog of quarantine pests for plants imported to the People’s Republic of China. Based on 1781 global distribution records of H. zea and eight bioclimatic variables, the potential geographical distributions (PGDs) of H. zea were predicted by using a calibrated MaxEnt model. The contribution rate of bioclimatic variables and the jackknife method were integrated to assess the significant variables governing the PGDs. The response curves of bioclimatic variables were quantitatively determined to predict the PGDs of H. zea under climate change. The results showed that: (1) four out of the eight variables contributed the most to the model performance, namely, mean diurnal range (bio2), precipitation seasonality (bio15), precipitation of the driest quarter (bio17) and precipitation of the warmest quarter (bio18); (2) PGDs of H. zea under the current climate covered 418.15 × 10⁴ km², and were large in China; and (3) future climate change will facilitate the expansion of PGDs for H. zea under shared socioeconomic pathways (SSP) 1-2.6, SSP2-4.5, and SSP5-8.5 in both the 2030s and 2050s. The conversion of unsuitable to low suitability habitat and moderately to high suitability habitat increased by 8.43% and 2.35%, respectively. From the present day to the 2030s, under SSP1-2.6, SSP2-4.5 and SSP5-8.5, the centroid of the suitable habitats of H. zea showed a general tendency to move eastward; from 2030s to the 2050s, under SSP1-2.6 and SSP5-8.5, it moved southward, and it moved slightly northward under SSP2-4.5. According to bioclimatic conditions, H. zea has a high capacity for colonization by introduced individuals in China. Customs ports should pay attention to host plants and containers of H. zea and should exchange information to strengthen plant quarantine and pest monitoring, thus enhancing target management.

Modeling the Climate Suitability of Northernmost Mangroves in China under Climate Change Scenarios

Mangroves are important wetland ecosystems on tropical and subtropical coasts. There is an urgent need to better understand how the spatial distribution of mangroves varies with climate change factors. Species distribution models can be used to reveal the spatial change of mangroves; however, global models typically have a horizontal resolution of hundreds of kilometers and more than 1 km, even after downscaling. In the present study, a maximum entropy model was used to predict suitable areas for the northernmost mangroves in China in the 2050s. An approach was proposed to improve the resolution and credibility of suitability predictions by incorporating land-use potential. Predictions were made based on two CMIP6 scenarios (i.e., SSP1-2.6 and SSP5-8.5). The results show that the northern edge of the natural mangrove distribution in China would migrate from 27.20° N to 27.39° N–28.15° N, and the total extent of suitable mangrove habitats would expand. By integrating 30 m resolution land-use data to refine the model’s predictions, under the SSP1-2.6 scenario, the suitable habitats of mangroves are predicted to be 13,435 ha, which would increase by 33.9% compared with the current scenario. Under the SSP5-8.5 scenario, the suitable area would be 23,120 ha, with an increased rate of 96.5%. Approximately 40–44% of the simulated mangrove patches would be adjacent to aquacultural ponds, cultivated, and artificial land, which may restrict mangrove expansion. Collectively, our results showed how climate change and land use could influence mangrove distributions, providing a scientific basis for adaptive mangrove habitat management despite climate change.

Ophiostomatoid species associated with pine trees (Pinus spp.) infested by Cryphaluspiceae from eastern China, including five new species

Cryphaluspiceae attacks various economically important conifers. Similar to other bark beetles, Cr.piceae plays a role as a vector for an assortment of fungi and nematodes. Previously, several ophiostomatoid fungi were isolated from Cr.piceae in Poland and Japan. In the present study, we explored the diversity of ophiostomatoid fungi associated with Cr.piceae infesting pines in the Shandong Province of China. We isolated ophiostomatoid fungi from both galleries and beetles collected from our study sites. These fungal isolates were identified using both molecular and morphological data. In this study, we recovered 175 isolates of ophiostomatoid fungi representing seven species. Ophiostomaips was the most frequently isolated species. Molecular and morphological data indicated that five ophiostomatoid fungal species recovered were previously undescribed. Thus, we proposed these five novel species as Ceratocystiopsisyantaiensis, C.weihaiensis, Graphilbumtranslucens, Gr.niveum, and Sporothrixvillosa. These new ophiostomatoid fungi add to the increasing number of fungi known from China, and this evidence suggests that numerous novel taxa are awaiting discovery in other forests of China.

Species Distribution Models of the Spartina alterniflora Loisel in Its Origin and Invasive Country Reveal an Ecological Niche Shift

Spartina alterniflora is a perennial herb native to the American Atlantic coast and is the dominant plant in coastal intertidal wetlands. Since its introduction to China in 1979, it has quickly spread along the coast and has caused various hazards. To control the further spread of S. alterniflora in China, we first reconstructed the history of the spread of S. alterniflora in its invasion and origin countries. We found that S. alterniflora spreads from the central coast to both sides of the coast in China, while it spreads from the west coast to the east coast in America. Furthermore, by comparing 19 environmental variables of S. alterniflora in its invasion and origin countries, it was found that S. alterniflora is more and more adaptable to the high temperature and dry environment in the invasion country. Finally, we predicted the suitable areas for this species in China and America using the maximum entropy (MaxEnt) model and ArcGIS. Overall, through analysis on the dynamic and trend of environmental characteristics during the invasion of S. alterniflora and predicting its suitable area in the invasion area, it guides preventing its reintroduction and preventing its further spread of the species has been found. It has reference significance for studying other similar alien plants and essential enlightening relevance to its invasion and spread in similar areas.

Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images

Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease’s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder–decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models.