Distribution Of Invasive Plant Species Research Articles

Modeling impacts of human footprint and soil variability on the potential distribution of invasive plant species in different biomes

Human footprint and soil variability may be important in shaping the spread of invasive plant species (IPS). However, until now, there is little knowledge on how human footprint and soil variability affect the potential distribution of IPS in different biomes. We used Maxent modeling to project the potential distribution of 29 IPS with wide distributions and long introduction histories in China based on various combinations of climatic correlates, soil characteristics and human footprint. Then, we evaluated the relative importance of each type of environmental variables (climate, soil and human footprint) as well as the difference in range and similarity of the potential distribution of IPS between different biomes. Human footprint and soil variables contributed to the prediction of the potential distribution of IPS, and different types of biomes had varying responses and degrees of impacts from the tested variables. Human footprint and soil variability had the highest tendency to increase the potential distribution of IPS in Montane Grasslands and Shrublands. We propose to integrate the assessment in impacts of human footprint and soil variability on the potential distribution of IPS in different biomes into the prevention and control of plant invasion.

Factors responsible for the distribution of invasive plant species in the surroundings of railway areas. A case study from SE Poland

AbstractInvasive plant species are a growing concern in global biodiversity management. In this study, we tried to determine whether invasive plants could spread from railway track into the surrounding areas and focused on the factors responsible for the invasive plant species composition, richness, and abundance. We sampled invasive species along transects located outside a railway route and recorded the invasive species richness and abundance at a distance of < 5 (control), 5–10, 10–15, and > 15 m away from the railway edge in three geographical regions in SE Poland. We revealed that the mechanism allowing alien species to invade was complex. Canonical correspondence analysis (CCA) explained 30.9% of variance. On a regional scale, the invasive flora composition in the surroundings of railway habitats was determined by abiotic environment diversity (mean annual air temperature, mean annual precipitation), but not habitat-related factors (soil type, soil properties). On a spatial scale, the traffic volume, the distance from the railway area, the distance from build-up areas, and the interaction of these factors affect the invasive species composition away from railway areas. The invasive species spread from railways to the surrounding vegetation, therefore, regular monitoring of railway areas should be a framework for assessing the risk of invasive species.

Opportunities and Constraints in Characterizing Landscape Distribution of an Invasive Grass from Very High Resolution Multi-Spectral Imagery.

Understanding spatial distributions of invasive plant species at early infestation stages is critical for assessing the dynamics and underlying factors of invasions. Recent progress in very high resolution remote sensing is facilitating this task by providing high spatial detail over whole-site extents that are prohibitive to comprehensive ground surveys. This study assessed the opportunities and constraints to characterize landscape distribution of the invasive grass medusahead (Elymus caput-medusae) in a ∼36.8 ha grassland in California, United States from 0.15m-resolution visible/near-infrared aerial imagery at the stage of late spring phenological contrast with dominant grasses. We compared several object-based unsupervised, single-run supervised and hierarchical approaches to classify medusahead using spectral, textural, and contextual variables. Fuzzy accuracy assessment indicated that 44–100% of test medusahead samples were matched by its classified extents from different methods, while 63–83% of test samples classified as medusahead had this class as an acceptable candidate. Main sources of error included spectral similarity between medusahead and other green species and mixing of medusahead with other vegetation at variable densities. Adding texture attributes to spectral variables increased the accuracy of most classification methods, corroborating the informative value of local patterns under limited spectral data. The highest accuracy across different metrics was shown by the supervised single-run support vector machine with seven vegetation classes and Bayesian algorithms with three vegetation classes; however, their medusahead allocations showed some “spillover” effects due to misclassifications with other green vegetation. This issue was addressed by more complex hierarchical approaches, though their final accuracy did not exceed the best single-run methods. However, the comparison of classified medusahead extents with field segments of its patches overlapping with survey transects indicated that most methods tended to miss and/or over-estimate the length of the smallest patches and under-estimate the largest ones due to classification errors. Overall, the study outcomes support the potential of cost-effective, very high-resolution sensing for the site-scale detection of infestation hotspots that can be customized to plant phenological schedules. However, more accurate medusahead patch delineation in mixed-cover grasslands would benefit from testing hyperspectral data and using our study’s framework to inform and constrain the candidate vegetation classes in heterogeneous locations.

Potential distribution of invasive alien species in the upper Ili river basin: determination and mechanism of bioclimatic variables under climate change

Controlling alien species invasion requires a full understanding of the potential distribution of alien species and the functions of key environmental factors that influence such distribution. In this study, potential distributions of 28 invasive alien species under current and future bioclimatic conditions at the upper Ili river basin (the Chinese part of the basin) were predicted, and the important bioclimatic variables that determine the distributions were examined. Under the current conditions, the number of alien species ranged from 0 to 10, and the eastern part of the basin may not be influenced by invaded species. Under future conditions, 0–22 alien species may find these areas suitable for invasion. The potential distributions of invasive plant species were determined mainly by precipitation-related bioclimatic variables, whereas temperature-related variables were relatively more relevant for animal species. Bioclimatic conditions during the coldest and/or driest month (or quarter, which is a period of 3 months) may significantly affect potential distributions for both plant and animal species. Quantified analysis demonstrated that the number of species is likely to increase considerably with the increase in precipitation during the driest month, particularly with a variation of >3 mm in the precipitation during the driest month. More alien species may invade regions where the precipitation during the driest quarter under future conditions is 10 mm higher than the current conditions. The effect of precipitation during the coldest quarter on species invasion is similar to that of precipitation during the driest quarter: the higher the precipitation during the driest quarter under future conditions (more than 9 mm than that under current conditions), the higher will be the rate of alien species invasion in the study area.

The importance of roads, nutrients, and climate for invasive plant establishment in riparian areas in the northwestern United States

Natural and anthropogenic site characteristics play a role in determining the current distribution of invasive plant species. An understanding of these characteristics can be used to prioritize areas for monitoring and control efforts and to determine appropriate management actions to lower site invasion risk. We used species distribution models to look for attributes associated with invasion and to determine the extent to which these attributes varied across a suite of species. We modeled the presence-absence of 11 invasive plant species along riparian areas in the northwestern United States using the model Random Forests. We found that climate variables were most important for predicting species distributions across the large study area and factors related to nutrients, land cover, and disturbance had moderate importance. We also found that there was a general pattern related to invasion for almost all species. Invasion was more likely to occur at hotter, drier sites near roads in unforested areas. In addition, high nutrient levels and proximity to streams with lower baseflow values also generally increased the likelihood that at least one invasive species would be present. Examining patterns across a broad range of regions can help suggest general mechanisms of invasion as well as provide region-specific management recommendations.

Distribution of Invasive Plant Species in Relation to Environmental and Anthropogenic Factors in Five Nature Reserves in Northern China

The relationships between invasive plant species and environmental and anthropogenic factors were analyzed in five nature reserves in northern China. The distribution of invasive plants was analyzed using a Canonical Correspondence Analysis. Stepwise backward multiple regression was then done to identify the most important environmental and anthropogenic factors determining the richness and distribution of alien plants in the reserves. Total invasive plant richness varied was 25 species, 12 species, 13 species, five species and five species across the nature reserves. Invasive species richness decreased in response to environmental factors such as maximum temperature and minimum precipitation and increased in line with human activities, the total area of farmland and volume of freight. Tropical plant species declined from 31% to zero while the species originally from North America gradually increased. In respect of family composition of invasive species, most species belonged to the composites, whose proportion increased from 15%–40% from north to south across the five nature reserves while species from other families varied greatly along this gradient. Life-forms of invasive plant species varied between the reserves, but annual and perennial herbs were generally dominant. Pathways of such species were highly related to human activities and utilizations.

Challenges in predicting the future distributions of invasive plant species

Species distribution models (SDMs) are increasingly used to predict distributions of invasive species. If successful, these models can help managers target limited resources for monitoring and controlling invasive species to areas of high invasion risk. Model accuracy is usually determined using current species distributions, but because invasive species are not at equilibrium with the environment, high current accuracy may not indicate high future accuracy. I used 1982 species distribution data from Bolleswood Natural Area, Connecticut, USA, to create SDMs for two forest invaders, Celastrus orbiculatus and Rosa multiflora. I then used more recent data, from 1992 and 2002, as validation data sets to determine how model accuracy changed over time and if current and future accuracy were related. I also tested if three alternative approaches – iterative modeling, alternative methods of choosing suitability thresholds and using a risk assessment framework – improved accuracy in predicting future distributions. Model accuracy declined over time with greater declines for models of the species (Celastrus) with the higher initial accuracy. By 2002, 49% of Celastrus and 85% of Rosa new occurrences were correctly predicted by models. Neither iterative modeling nor alternative thresholds improved accuracy of predicting 2002 occurrences, but a risk assessment framework showed promise for guiding monitoring efforts. These results suggest that measures of current accuracy may not indicate a model’s predictive accuracy and must be used cautiously. Distinguishing between predictions of current and future distributions is critical. While iterative models were not successful in this study, I argue that using models in a risk assessment framework closely tied to monitoring will greatly increase the utility of SDMs for managing invasive species.

Floristic analysis of perennial species on flowerbeds in Belgrade with special attention on invasiveness of the recorded species

Urban areas are among the most important centres of invasive plant species distribution due to their richness in alien species. Because of that, a detailed floristic analysis of perennial flowerbeds was conducted in the central parks of Belgrade. A total of 53 perennial species were found, of which 55% were the alien species planted on 75% of the research area. Among them, two species (Aster novi belgii and Solidago canadensis) are invasive and six species are potentially invasive in Serbia. These are planted on 5% and 20% of the flowerbeds, respectively. We can conclude that both the experts and institutions should be informed about the invasive species and potential damages. In the meantime, planting of native decorative species should be encouraged, since they will not pose a threat to natural habitats. Also, detailed research should be conducted in order to eradicate invasive and potentially invasive species from the surfaces around the research area.

Combining local‐ and large‐scale models to predict the distributions of invasive plant species

Habitat distribution models are increasingly used to predict the potential distributions of invasive species and to inform monitoring. However, these models assume that species are in equilibrium with the environment, which is clearly not true for most invasive species. Although this assumption is frequently acknowledged, solutions have not been adequately addressed. There are several potential methods for improving habitat distribution models. Models that require only presence data may be more effective for invasive species, but this assumption has rarely been tested. In addition, combining modeling types to form "ensemble" models may improve the accuracy of predictions. However, even with these improvements, models developed for recently invaded areas are greatly influenced by the current distributions of species and thus reflect near- rather than long-term potential for invasion. Larger scale models from species' native and invaded ranges may better reflect long-term invasion potential, but they lack finer scale resolution. We compared logistic regression (which uses presence/absence data) and two presence-only methods for modeling the potential distributions of three invasive plant species on the Olympic Peninsula in Washington, USA. We then combined the three methods to create ensemble models. We also developed climate envelope models for the same species based on larger scale distributions and combined models from multiple scales to create an index of near- and long-term invasion risk to inform monitoring in Olympic National Park (ONP). Neither presence-only nor ensemble models were more accurate than logistic regression for any of the species. Larger scale models predicted much greater areas at risk of invasion. Our index of near- and long-term invasion risk indicates that < 4% of ONP is at high near-term risk of invasion while 67-99% of the Park is at moderate or high long-term risk of invasion. We demonstrate how modeling results can be used to guide the design of monitoring protocols and monitoring results can in turn be used to refine models. We propose that, by using models from multiple scales to predict invasion risk and by explicitly linking model development to monitoring, it may be possible to overcome some of the limitations of habitat distribution models.

A global comparison of plant invasions on oceanic islands

Abstract Oceanic islands have long been considered to be particularly vulnerable to biotic invasions, and much research has focused on invasive plants on oceanic islands. However, findings from individual islands have rarely been compared between islands within or between biogeographic regions. We present in this study the most comprehensive, standardized dataset to date on the global distribution of invasive plant species in natural areas of oceanic islands. We compiled lists of moderate (5–25% cover) and dominant (>25% cover) invasive plant species for 30 island groups from four oceanic regions (Atlantic, Caribbean, Pacific, and Western Indian Ocean). To assess consistency of plant behaviour across island groups, we also recorded present but not invasive species in each island group. We tested the importance of different factors discussed in the literature in predicting the number of invasive plant species per island group, including island area and isolation, habitat diversity, native species diversity, and human development. Further we investigated whether particular invasive species are consistently and predictably invasive across island archipelagos or whether island-specific factors are more important than species traits in explaining the invasion success of particular species. We found in total 383 non-native spermatophyte plants that were invasive in natural areas on at least one of the 30 studied island groups, with between 3 and 74 invaders per island group. Of these invaders about 50% (181 species) were dominants or co-dominants of a habitat in at least one island group. An extrapolation from species accumulation curves across the 30 island groups indicates that the total current flora of invasive plants on oceanic islands at latitudes between c. 35°N and 35°S may eventually consist of 500–800 spermatophyte species, with 250–350 of these being dominant invaders in at least one island group. The number of invaders per island group was well predicted by a combination of human development (measured by the gross domestic product (GDP) per capita), habitat diversity (number of habitat types), island age, and oceanic region (87% of variation explained). Island area, latitude, isolation from continents, number of present, non-native species with a known invasion history, and native species richness were not retained as significant factors in the multivariate models. Among 259 invaders present in at least five island groups, only 9 species were dominant invaders in at least 50% of island groups where they were present. Most species were invasive only in one to a few island groups although they were typically present in many more island groups. Consequently, similarity between island groups was low for invader floras but considerably higher for introduced (but not necessarily invasive) species – especially in pairs of island groups that are spatially close or similar in latitude. Hence, for invasive plants of natural areas, biotic homogenization among oceanic islands may be driven by the recurrent deliberate human introduction of the same species to different islands, while post-introduction processes during establishment and spread in natural areas tend to reduce similarity in invader composition between oceanic islands. We discuss a number of possible mechanisms, including time lags, propagule pressure, local biotic and abiotic factors, invader community assembly history, and genotypic differences that may explain the inconsistent performance of particular invasive species in different island groups.

Multivariate forecasts of potential distributions of invasive plant species

The fact that plant invasions are an ongoing process makes generalizations of invasive spread extraordinarily challenging. This is particularly true given the idiosyncratic nature of invasions, in which both historical and local conditions affect establishment success and hinder our ability to generate guidelines for early detection and eradication of invasive species. To overcome these limitations we have implemented a comprehensive approach that examines plant invasions at three spatial scales: regional, landscape, and local levels. At each scale, in combination with the others, we have evaluated the role of key environmental variables such as climate, landscape structure, habitat type, and canopy closure in the spread of three commonly found invasive woody plant species in New England, Berberis thunbergii, Celastrus orbiculatus, and Euonymus alatus. We developed a spatially explicit hierarchical Bayesian model that allowed us to take into account the ongoing nature of the spread of invasive species and to incorporate presence/absence data from the species' native ranges as well as from the invaded regions. Comparisons between predictions from climate-only models with those from the multiscale forecasts emphasize the importance of including landscape structure in our models of invasive species' potential distributions. In addition, predictions generated using only native range data performed substantially worse than those that incorporated data from the target range. This points out important limitations in extrapolating distributional ranges from one region to another.

Developing an approach to defining the potential distributions of invasive plant species: a case study of Hakea species in South Africa

ABSTRACTAim Models of the potential distributions of invading species have to deal with a number of issues. The key one is the high likelihood that the absence of an invading species in an area is a false absence because it may not have invaded that area yet, or that it may not have been detected. This paper develops an approach for screening pseudo‐absences in a way that is logical and defensible.Innovation The step‐wise approach involves: (1) screening environmental variables to identify those most likely to indicate conditions where the species cannot invade; (2) identifying and selecting the most likely limiting variables; (3) using these to define the limits of its invasion potential; and (4) selecting points outside these limits as true absence records for input into species distribution models.This approach was adopted and used for the study of three prominent Hakea species in South Africa. Models with and without the false absence records were compared. Two rainfall variables and the mean minimum temperature of the coldest month were the strongest predictors of potential distributions. Models which excluded false absences predicted that more of the potential distribution would have a high invasion potential than those which included them.Main conclusions The approach of applying a priori knowledge can be useful in refining the potential distribution of a species by excluding pseudo‐absence records which are likely to be due to the species not having invaded an area yet or being undetected. The differences between the potential distributions predicted by the different models convey more information than making a single prediction, albeit a consensus model. The robustness of this approach depends strongly on an adequate knowledge of the ecology, invasion history and current distribution of that species.

The role of native riparian tree species in decomposition of invasive tree of heaven (Ailanthus altissima) leaf litter in an urban stream

Increasingly, interactions between human and natural systems centre on the multi-scale restoration of ecosystems. Humans rely on ecosystem services provided by streams, yet human activities degrade water quality worldwide. Re-planting streamside vegetation is a common restoration practice, since trees reduce runoff and stabilize banks. But does riparian tree biodiversity matter? Detrital inputs from riparian vegetation impact in-stream processes, e.g., leaf decomposition. Since the increasing distribution of invasive plant species alters the structure of streamside forest communities, input of invasive litter to streams could alter such processes. We followed decomposition rates of the invasive tree of heaven (Ailanthus altissima, TOH) and 6 native leaf species in an urban stream and complemented this effort with laboratory feeding experiments employing the same treatments and 2 common aquatic detritivores. TOH breakdown was rapid, exceeding native leaf decay. Mixing TOH with native species reduced its decay compared to TOH alone; however, the feeding study demonstrated that detritivores preferred TOH over native species. Subsequent estimates of species-specific structural integrity revealed TOH poorly resisted breakage. The relatively tougher nature of native species may slow TOH breakdown by armouring the invasive litter against the highly variable flow regime characteristic of urban streams. The presence of native riparian tree species may mediate how invasive trees decompose in human-impacted streams.

The biogeography of invasive alien plants in California: an application of GIS and spatial regression analysis

ABSTRACTThe spatial distribution of invasive alien plants has been poorly documented in California. However, with the increased availability of GIS software and spatially explicit data, the distribution of invasive alien plants can be explored. Using bioregions as defined in Hickman (1993), I compared the distribution of invasive alien plants (n = 78) and noninvasive alien plants (n = 1097). The distribution of both categories of alien plants was similar with the exception of a higher concentration of invasive alien plants in the North Coast bioregion. Spatial autocorrelation analysis using Moran's I indicated significant spatial dependence for both invasive and noninvasive alien plant species. I used both ordinary least squares (OLS) and spatial autoregressive (SAR) models to assess the relationship between alien plant species distribution and native plant species richness, road density, population density, elevation, area of sample unit, and precipitation. The OLS model for invasive alien plants included two significant effects; native plant species richness and elevation. The SAR model for invasive alien plants included three significant effects; elevation, road density, and native plant species richness. The SAR model for noninvasive alien plants resulted in the same significant effects as invasive alien plants. Both invasive and noninvasive alien plants are found in regions with low elevation, high road density, and high native‐plant species richness. This is in congruity with previous spatial pattern studies of alien plant species. However, the similarity in effects for both categories of alien plants alludes to the importance of autecological attributes, such as pollination system, dispersal system and differing responses to disturbance in the distribution of invasive plant species. In addition, this study emphasizes the critical importance of testing for spatial autocorrelation in spatial pattern studies and using SAR models when appropriate.