Modeling potential hotspots of invasive Prosopis juliflora (Swartz) DC in India

Prediction of potential invasion of two weeds of the genus Avena in Asia under climate change based on Maxent

Avena sterilis L. (A. sterilis) and Avena ludoviciana Dur. (A. ludoviciana) are extremely invasive weeds with strong competitive ability and multiple transmission routes. Both species can invade a variety of dryland crops, including wheat, corn, and beans. Asia, as the world's major food-producing continent, will experience significant losses to agricultural production if it is invaded by these weeds on a large scale. This study used the MaxEnt model and ArcGIS to map the distribution of suitable habitats of the two species in Asia under climate change conditions. The constructed model comprised four levels, with a total of 25 index-level indicator factors used to evaluate the invasion risk of the two species. The results showed that the distribution of suitable habitats for both Avena species was highly dependent on precipitation and temperature. Under climate warming conditions, although overall the total suitable area is predicted to decrease compared to the current period, there are still moderately or highly suitable areas. Asian countries need to provide early warning for areas with significant increases in moderate and highly suitable zones for these two species of weeds under the background of climate change. If there is already an invaded area or if the suitability of the original area is increased, this should be closely monitored, and control measures should be taken to prevent further spread and deterioration.

Unraveling the ecological threads: How invasive alien plants influence soil carbon dynamics

Invasive alien plants (IAPs) pose significant threats to native ecosystems and biodiversity worldwide. However, the understanding of their precise impact on soil carbon (C) dynamics in invaded ecosystems remains a crucial area of research. This review comprehensively explores the mechanisms through which IAPs influence soil C pools, fluxes, and C budgets, shedding light on their effects and broader consequences. Key mechanisms identified include changes in litter inputs, rates of organic matter decomposition, alterations in soil microbial communities, and shifts in nutrient cycling, all driving the impact of IAPs on soil C dynamics. These mechanisms affect soil C storage, turnover rates, and ecosystem functioning. Moreover, IAPs tend to increase gross primary productivity and net primary productivity leading to the alterations in fluxes and C budgets. The implications of IAP-induced alterations in soil C dynamics are significant and extend to plant-soil interactions, ecosystem structure, and biodiversity. Additionally, they have profound consequences for C sequestration, potentially impacting climate change mitigation. Restoring native plant communities, promoting soil health, and implementing species-specific management are essential measures to significantly mitigate the impacts of IAPs on soil C dynamics. Overall, understanding and mitigating the effects of IAPs on soil C storage, nutrient cycling, and related processes will contribute to the conservation of native biodiversity and complement global C neutrality efforts.

Global spatial distribution of Prosopis juliflora - one of the world's worst 100 invasive alien species under changing climate using multiple machine learning models

Climate change is one of the factors contributing to the spread of invasive alien species. As a result, it is critical to investigate potential invasion dynamics on a global scale in the face of climate change. We used updated occurrence data, bioclimatic variables, and Köppen-Geiger climatic zones to better understand the climatic niche dynamics of Prosopis juliflora L. (Fabaceae). In this study, we first compared several algorithms-MaxEnt, generalized linear model (GLM), artificial neural network (ANN), generalized boosted model (GBM), generalized additive model (GAM), and random forest (RF)-to investigate the relationships between species-environment and climate for mesquite. We identified the global climate niche similarity sites (NSSs) using the coalesce approach. This study focused on the current and future climatic suitability of P. juliflora under two global circulation models (GCMs) and two climatic scenarios, i.e., Representative Concentration Pathways (RCPs), 4.5 and 8.5, for 2050 and 2070, respectively. Sensitivity, specificity, true skill statistic (TSS), kappa coefficient, and correlation were used to evaluate model performance. Among the tested models, the machine learning algorithm random forest (RF) demonstrated the highest accuracy. The vast swaths of currently uninvaded land on multiple continents are ideal habitats for invasion. Approximately 9.65% of the area is highly suitable for the establishment of P. juliflora. Consequently, certain regions in the Americas, Africa, Asia, Europe, and Oceania have become particularly vulnerable to invasion. In relation to RCPs, we identified suitable area changes (expansion, loss, and stability). The findings of this study show that NSSs and RCPs increase the risk of invasion in specific parts of the world. Our findings contribute to a cross-border continental conservation effort to combat P. juliflora  expansion into new potential invasion areas.

Potentially suitable distribution areas of Populus euphratica and Tamarix chinensis by MaxEnt and random forest model in the lower reaches of the Heihe River, China

Populus euphratica and Tamarix chinensis play a vital role in windbreak and sand fixation, maintaining species diversity and ensuring community stability. Managing and protecting the P. euphratica and T. chinensis forests in the Heihe River's lower reaches is an urgent issue to maintain the desert region's ecological balance. In this study, based on the distribution points of P. euphratica and T. chinensis species and environmental data, MaxEnt and random forest (RF) models were used to characterize the potential distribution areas of P. euphratica and T. chinensis in the lower reaches of the Heihe River. The results showed that the accuracy of the RF model was much higher than that of the MaxEnt model. Both the RF and MaxEnt models showed that the distance to the river greatly influenced the distribution of P. euphratica and T. chinensis. Furthermore, the RF model predicted significantly larger highly suitable areas for both P. euphratica and T. chinensis than the MaxEnt model. Our study enhances the understanding of the species' spatial distribution, offering valuable insights for practical management and conservation strategies.

Mapping the Environmental Risk of Beech Leaf Disease in the Northeastern United States

The recently emerged beech leaf disease (BLD) is causing the decline and death of American beech in North America. First observed in 2012 in northeast Ohio, U.S.A., BLD had been documented in 10 northeastern states and the Canadian province of Ontario as of July 2022. A foliar nematode has been implicated as the causal agent, along with some bacterial taxa. No effective treatments have been documented in the primary literature. Irrespective of potential treatments, prevention and prompt eradication (rapid responses) remain the most cost-effective approaches to the management of forest tree disease. For these approaches to be feasible, however, it is necessary to understand the factors that contribute to BLD spread and use them in estimation of risk. Here, we conducted an analysis of BLD risk across northern Ohio, western Pennsylvania, western New York, and northern West Virginia, U.S.A. In the absence of symptoms, an area cannot necessarily be deemed free of BLD (i.e., absence of BLD cannot be certain) due to its fast spread and the lag in symptom expression (latency) after infection. Therefore, we employed two widely used presence-only species distribution models (SDMs), one-class support vector machine (OCSVM), and maximum entropy (Maxent) to predict the spatial pattern of BLD risk based on BLD presence records and associated environmental variables. Our results show that both methods work well for BLD environmental risk modeling purposes, but Maxent outperforms OCSVM with respect to both the quantitative receiver operating characteristics (ROC) analysis and the qualitative evaluation of the spatial risk maps. Meanwhile, the Maxent model provides a quantification of variable contribution for different environmental factors, indicating that meteorological (isothermality and temperature seasonality) and land cover type (closed broadleaved deciduous forest) factors are likely key contributors to BLD distribution. Moreover, the future trajectories of BLD risk over our study area in the context of climate change were investigated by comparing the current and future risk maps obtained by Maxent. In addition to offering the ability to predict where the disease may spread next, our work contributes to the epidemiological characterization of BLD, providing new lines of investigation to improve ecological or silvicultural management. Furthermore, this study shows strong potential for extension of environmental risk mapping over the full American beech distribution range so that proactive management measures can be put in place. Similar approaches can be designed for other significant or emerging forest pest problems, contributing to overall management efficiency and efficacy.

Assessing risk from invasive alien plants in China: Reconstructing invasion history and estimating distribution patterns of Lolium temulentum and Aegilops tauschii

INTRODUCTION

The establishment of invasive alien plants (IAPs) is primarily driven by climate warming and human activities, and their populations have a negative impact on agricultural economics, ecological systems, and human health. Lolium temulentum and Aegilops tauschii are critical IAPs in China because they reduce the quality of cereal grains and decrease wheat yields. Lolium temulentum is a winter-temperate weed that spreads easily and is poisonous to humans and animals. Aegilops tauschii is resistant to herbicides, has a high reproductive rate, and frequently grows in wheat. Both species have been listed in the Ministry of Agriculture and Rural Affairs of the People's Republic of China's management catalog since 2006.

METHODS

In the present study, the historical occurrence and invasion of each species were collected and reconstructed, which showed that the population outbreak of L. temulentum began in 2010, whereas that of A. tauschii began in 2000. Using the optimal MaxEnt model, the geographical distributions of L. temulentum and A. tauschii were predicted based on screened species occurrences and environmental variables under the current and three future scenarios in the 2030s and 2050s (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5).

RESULTS

The mean AUC values were 0.867 and 0.931 for L. temulentum and A. tauschii, respectively. Human influence index (HII), mean temperature of coldest quarter (bio11), and precipitation of coldest quarter (bio19) were the most significant variables for L. temulentum, whereas human influence index, temperature seasonality (standard deviation×100) (bio4), and annual mean temperature (bio1) were the critical environmental variables for A. tauschi. Suitable habitat areas in China for L. temulentum and A. tauschii currently covered total areas of 125 × 10⁴ and 235 × 10⁴ km², respectively. Future suitable areas of L. temulentum reached the maximum under SSP2-4.5, from 2021 to 2060, whereas for A. tauschii they reached the maximum under SSP5-8.5, from 2021 to 2060. Furthermore, the overlap area under the current climate conditions for L. temulentum and A. tauschii was approximately 90 × 10⁴ km², mainly located in Hubei, Anhui, Jiangsu, Shandong, Henan, Shaanxi, Shanxi, and Hebei. The overlap areas decreased in the 2030s, increased in the 2050s, and reached a maximum under SSP1-2.6 (or SSP2-4.5) with an approximate area of 104 × 10⁴ km². The centroid of L. temulentum in Henan was transferred to the southwest, whereas for A. tauschii it transferred to higher latitudes in the northeast.

DISCUSSION

Our findings provide a practical reference for the early warning, control, and management of these two destructive IAP populations in China.