Effects of occurrence record number, environmental variable number, and spatial scales on MaxEnt distribution modelling for invasive plants

Contrasting range changes and drivers of four forest foundation species under future climate change in China

Forest foundation species, vital for shaping community structure and dynamics through non-trophic level interactions, are key to forest succession and sustainability. Despite their ecological importance, the habitat ranges of these species in China and their responses to future climate change remain unclear. Our study employed the optimal MaxEnt model to assess the range shifts and their essential drivers of four typical forest foundation species from three climatic zones in China under climate scenarios, including Acer tegmentosum, Acer pseudo-sieboldianum (temperate zone), Quercus glandulifera (subtropical zone), and Ficus hispida (tropical zone). The optimal MaxEnt model exhibited high evaluation indices (AUC values > 0.90) for the four foundation species, indicating excellent predictive performance. Currently, we observed that A. tegmentosum and A. pseudo-sieboldianum are predominantly inhabited temperate forest areas in northeastern China, Q. glandulifera is primarily concentrated in subtropical forests in southeastern China, and F. hispida is mainly distributed across the tropical forests in southern China. Climate factors, particularly temperature, emerged as the primary environmental factors influencing the potential range of forest foundation species. Moreover, precipitation strongly influenced the potential range of A. tegmentosum and A. pseudo-sieboldianum, while elevation exhibited a greater impact on the range of Q. glandulifera and F. hispida. Under future climate scenarios, suitable areas for A. tegmentosum and A. pseudo-sieboldianum tend to expand southward, F. hispida tends to expand northward, while Q. glandulifera exhibited a tendency to contract towards the center. This study advances our understanding of the spatial and temporal dynamics of forest foundation species in China under climate change, providing critical insights for conservation efforts and sustainable forest management practices.

Potential impact of climatic factors on the distribution of Graphium sarpedon in China

Graphium sarpedon is a significant foliar pest of Laurel plants in China. In this study, the MaxEnt model was used to investigate the distribution of G. sarpedon and predict its potential distribution areas in China in the future (2050s and 2090s) based on three Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, and SSP5-8.5), and key environmental variables affecting its distribution were identified. The results showed that under the current climatic conditions, the suitable distribution areas of G. sarpedon were 92.17°-134.96° E and 18.04°-33.61° N, including Yangtze Plain (Middle and Lower), Pearl River Delta, Yangtze River Delta, and Lingnan areas. Under the future climate conditions, the total suitable distribution area of G. sarpedon decreased, but the area of medium suitable area increased. The study identified 11 key environmental variables affecting the distribution of G. sarpedon, the most critical of which was Precipitation of Warmest Quarter (bio18) and precipitation in April, May, June, and September (prec4, prec5, prec6, and prec9). This study is beneficial for monitoring and preventing the possible changes of G. sarpedon and provides theoretical references for its prevention and control.

Prediction of the Current and Future Distribution of Tomato Leafminer in China Using the MaxEnt Model

Tomato leafminer (Tuta absoluta), an important quarantine pest in China, was first detected in China in Yili, Xinjiang Uygur Autonomous Region, in 2017. Its damage has grown in recent years, severely harming Solanaceae plants in China and causing enormous economic losses. The study and prediction of the current and future suitable habitats for tomato leafminer in China can provide an important reference for the monitoring, early warning, and prevention and control of the pest. Here, tomato leafminer's potential distributions in China under the current climate and four future climate models (SSP1-26, SSP2-45, SSP3-70, and SSP5-85) were predicted using the maximum entropy (MaxEnt) model with ArcGIS software, and the accuracy of the prediction results was tested. The areas under the receiver operating characteristic curves of the models were all greater than 0.8, and the test omission rate of the model simulation results basically agreed with the theoretical omission rate, suggesting that the prediction results had satisfactory accuracy and reliability. Under the current climatic conditions, the highly suitable habitats for tomato leafminer in China are mainly distributed in most of North China, most of East China, most of South China, most of Central China, most of Southwest China, some parts of Northeast China, and only a few parts of Northwest China. Annual mean temperature is the main environmental factor limiting the distribution. The suitable habitats for tomato leafminer will shift under different future climate models: Under SSP1-26, the highly suitable habitats will spread to the north and northeast and to the southeast coastal areas; under SSP2-45, the size of highly suitable habitats will grow from the present to 2080 and shrink from 2081 to 2100; under SSP3-70, the highly suitable habitats will spread northeastwards, but the highly suitable habitats in southeast coastal areas will shrink from 2081 to 2100 and turn into moderately suitable habitats. Under SSP5-85, the highly suitable habitats will spread northeastwards and northwestwards, with the size of highly suitable habitats gradually decreasing and the size of moderately suitable habitats increasing. Different climates will lead to different distributions of suitable habitats for tomato leafminer, with annual mean temperature, isothermality, and mean diurnal range as the main environmental influences.

Comparing the Performance of CMCC-BioClimInd and WorldClim Datasets in Predicting Global Invasive Plant Distributions

Species distribution modeling (SDM) has been widely used to predict the distribution of invasive plant species based on bioclimatic variables. However, the specific selection of these variables may affect the performance of SDM. This investigation elucidates a new bioclimate variable dataset (i.e., CMCC-BioClimInd) for its use in SDM. The predictive performance of SDM that includes WorldClim and CMCC-BioClimInd was evaluated by AUC and omission rate and the explanatory power of both datasets was assessed by the jackknife method. Furthermore, the ODMAP protocol was used to record CMCC-BioClimInd to ensure reproducibility. The results indicated that CMCC-BioClimInd effectively simulates invasive plant species' distribution. Based on the contribution rate of CMCC-BioClimInd to the distribution of invasive plant species, it was inferred that the modified and simplified continentality and Kira warmth index from CMCC-BioClimInd had a strong explanatory power. Under the 35 bioclimatic variables of CMCC-BioClimInd, alien invasive plant species are mainly distributed in equatorial, tropical, and subtropical regions. We tested a new bioclimate variable dataset to simulate the distribution of invasive plant species worldwide. This method has great potential to improve the efficiency of species distribution modeling, thereby providing a new perspective for risk assessment and management of global invasive plant species.

Prediction of global potential suitable habitats of Nicotiana alata Link et Otto based on MaxEnt model

Nicotiana alata Link et Otto, widely used in landscaping, is not only of great ornamental value but also of high commercial and medical value. The global potential habitat of N. alata and the environmental factors affecting its distribution are not that clear at present. To provide a reference for the reasonable and extensive planting of N. alata now and in the future, the MaxEnt model was used to predict its global suitable habitats under current and future climate conditions, respectively, based on global geographic distribution data of N. alata and the current and future world bioclimatic variables. The results showed that mean temperature of the driest quarter (bio9), precipitation of driest month (bio14), precipitation seasonality (bio15) and max temperature of warmest month (bio5), were the key bioclimatic variables governing the distribution of N. alata. The global suitable habitats of N. alata were mainly distributed in Europe, the United States, southeastern South America, and China under current climate conditions. Compared with current climate conditions, the future climate decreased suitable habitats of N. alata under SSP1-2.6, and SSP2-4.5 scenario and increased suitable habitats of N. alata under SSP3-7.0, and SSP5-8.5 climatic scenarios. The results provided valuable information and theoretical reference for the reasonable planting of N. alata.

Plant Species' Capacity for Range Shifts at the Habitat and Geographic Scales: A Trade-Off-Based Framework

Climate change is causing rapid shifts in the abiotic and biotic environmental conditions experienced by plant populations, but we lack generalizable frameworks for predicting the consequences for species. These changes may cause individuals to become poorly matched to their environments, potentially inducing shifts in the distributions of populations and altering species' habitat and geographic ranges. We present a trade-off-based framework for understanding and predicting whether plant species may undergo range shifts, based on ecological strategies defined by functional trait variation. We define a species' capacity for undergoing range shifts as the product of its colonization ability and the ability to express a phenotype well-suited to the environment across life stages (phenotype-environment matching), which are both strongly influenced by a species' ecological strategy and unavoidable trade-offs in function. While numerous strategies may be successful in an environment, severe phenotype-environment mismatches result in habitat filtering: propagules reach a site but cannot establish there. Operating within individuals and populations, these processes will affect species' habitat ranges at small scales, and aggregated across populations, will determine whether species track climatic changes and undergo geographic range shifts. This trade-off-based framework can provide a conceptual basis for species distribution models that are generalizable across plant species, aiding in the prediction of shifts in plant species' ranges in response to climate change.

Spatial weed distribution models under climate change: a short review

Climate change is a concern worldwide that could trigger many changes with severe consequences. Since human demography is steadily increasing, agriculture has to be constantly investigated to aim at improving its efficiency. Weeds play a key role in this task, especially in the recent past and at present, when new introductions have been favoured by a rise in tourism and international trade. To obtain knowledge relating weeds and their behaviour to climate change, species distribution models (SDMs) have also increased recently. In this work, we have reviewed some articles published since 2017 on modelled weeds, aiming to give a response to, among other things, the species most studied, the scale and location of the studies, the algorithms used and validation parameters, global change scenarios, types of variables, and the sources from which the data were collected. Fifty-nine articles were selected to be reviewed, with maximum entropy (MaxEnt) and area under the curve (AUC) being the most popular software and validation processes. Environmental and topographic variables were considered above pedological and anthropogenic ones. Europe was the continent and China, the USA, and India the countries most studied. In this review, it was found that the number of published articles between developed and developing countries is unbalanced and comes out in favour of the former. The current knowledge on this topic can be considered to be good not enough, especially in developing countries with high population densities. The more knowledge we can obtain, the better our understanding is of how to deal with this issue, which is a worldwide preoccupation.

Quo Vadis, Orthotrichum pulchellum? A Journey of Epiphytic Moss across the European Continent

Orthotrichum pulchellum is a species of epiphytic moss in which a significant expansion from the oceanic part of Europe to the east of the continent has been observed in the recent two decades. The improvement in air quality in Central and Eastern Europe, but also climate change, probably plays a role in this. This study shows what direction of its spreading we can expect in the future. Ecological niche modeling (ENM) is a widespread method to find out species niches in environmental and geographical space, which allows us to highlight areas that have a higher probability of occurrences of the studied species, based on identifying similar environmental conditions to those already known. We also made predictions for different future scenarios (CMIP5 climatology datasets for the years 2041-2060). Because we were not able to distinguish between historical and newly settled areas, and so, had to use some of the traditional approaches when modeling invasive species, we proposed to use niche clusters based on environmental layers to split the data of all known occurrences and make models separately for each cluster. This approach seems reasonable from the ecological species point of view because using all the morphologically same samples could be misleading. Altogether, 2712 samples were used from three separate niche clusters. For building the models, the Maxent algorithm was used as a well-tested, well-accepted, and commonly used method.

Distribution Patterns of Invasive Buffelgrass (Cenchrus ciliaris) in Mexico Estimated with Climate Niche Models under the Current and Future Climate

In Mexico, buffelgrass (Cenchrus ciliaris) was introduced in the middle of the 20th century. Currently, buffelgrass has become an invasive species and has colonized various ecosystems in the country. In addition to its invasive capacity, climate change is a factor that has to be taken into account when considering how to effectively manage and control this species. The climatic niche models (CNM) and their projections for climate change scenarios allow for estimating the extent of biological invasions. Our study aimed to calibrate a CNM for buffelgrass in Mexico under the current climatic conditions and to project the extent of its biological invasion under climate change scenarios. For that, we used MaxEnt to generate the current CNM and to detect if climate change could cause future changes, we then evaluated the distribution patterns over the periods of 2041–2060, 2061–2080, and 2081–2100 for all the shared socioeconomic pathways (SSPs). Linear regressions were used to compare the outputs between current and future scenarios. Under the current climate, the CNM estimated that 42.2% of the continental surface of Mexico is highly suitable for buffelgrass. The regression analyses indicated no effects from climate change on the distribution of buffelgrass. Moreover, when the projected period is further in the future, and when the SSPs intensify, the surface of suitable areas for the species increases. These analyses clearly suggest Mexico is facing a biological invasion from buffelgrass, which may represent a threat to native biodiversity.

Using Consensus Land Cover Data to Model Global Invasive Tree Species Distributions

Invasive tree species threaten ecosystems, natural resources, and managed land worldwide. Land cover has been widely used as an environmental variable for predicting global invasive tree species distributions. Recent studies have shown that consensus land cover data can be an effective tool for species distribution modelling. In this paper, consensus land cover data were used as prediction variables to predict the distribution of the 11 most aggressive invasive tree species globally. We found that consensus land cover data could indeed contribute to modelling the distribution of invasive tree species. According to the contribution rate of land cover to the distribution of invasive tree species, we inferred that the cover classes of open water and evergreen broadleaf trees have strong explanatory power regarding the distribution of invasive tree species. Under consensus land cover changes, invasive tree species were mainly distributed near equatorial, tropical, and subtropical areas. In order to limit the damage caused by invasive tree species to global biodiversity, human life, safety, and the economy, strong measures must be implemented to prevent the further expansion of invasive tree species. We suggest the use of consensus land cover data to model global invasive tree species distributions, as this approach has strong potential to enhance the performance of species distribution modelling. Our study provides new insights into the risk assessment and management of invasive tree species globally.

Modelling and validation of the spatial distribution of suitable habitats for the recruitment of invasive plants on climate change scenarios: An approach from the regeneration niche

The regeneration niche concept states that plant species only occur in habitats where the environmental conditions allow their recruitment. This study focuses on this concept and proposes a novel approach for modelling and experimentally validating the distribution of suitable habitats for the recruitment of invasive plants under the current and future climate. The biological invasion of the Peruvian peppertree (Schinus molle) in Mexico is used as practical example. The values of eight bioclimatic variables associated to sites in which young, naturally established seedlings and saplings were detected were used to model the current distribution of recruitment habitats. A machine-learning algorithm of maximum entropy (MaxEnt) was used to calibrate the model and its output indicated the distribution of occurrence probabilities of young peppertrees in Mexico under the current climate. This model was projected on climate change scenarios predicted for the middle of this century, which indicated that the cover of suitable recruitment habitats for this invasive species will shrink. To validate these predictions, field experiments were performed at three sites where the model predicted reduced occurrence probabilities of young peppertrees. In these experiments, emergence and survival rates of peppertree seedlings were assessed under the current climate and under simulated climate change conditions. As seedling emergence and survival rates were lower under simulated climate change conditions, the experiments validated the model predictions. These results supported our proposal, which combines modelling and experimental approaches to make accurate and valid predictions about the distribution of suitable recruitment habitats for invasive plants in a warmer and drier world.