Habitat suitability modelling of an invasive plant with advanced remote sensing data

Aim  Lepidium latifolium (Brassicaceae; perennial pepperweed) is a noxious Eurasian weed invading riparian and wetland areas of the western USA. Understanding which sites are most susceptible to invasion by L. latifolium will allow more efficient management of this weed. We assessed the ability of advanced remote sensing techniques to develop habitat suitability models for L. latifolium .
Location  San Francisco Bay/Sacramento-San Joaquin River Delta, California, USA.
Methods  Lepidium latifolium distribution was mapped with hyperspectral image data of Rush Ranch Open Space Preserve, providing presence/absence data to train and validate habitat models. A high-resolution light detection and ranging digital elevation model was used to derive predictor environmental variables (distance to channel, distance to upland, elevation, slope, aspect and convexity). Aggregate decision tree models were used to predict the potential distribution of this species.
Results  Lepidium latifolium infested two zones: near the marshland–upland margin and along channels within the marsh. Topographical data, which are typically strongly correlated with wetland species distributions, were relatively unimportant to L. latifolium occurrence, although relevant microtopography information, particularly relative elevation, was subsumed in the distance to channel variable. The map of potential L. latifolium distribution reveals that Rush Ranch contains considerable habitat that it is susceptible to continued invasion.
Main conclusions  Lepidium latifolium invades relatively less stressful sites along the inundation and salinity gradients. Advanced remote sensing datasets were shown to be sufficient for species distribution modelling. Remote sensing offers powerful tools that deserve wider use in ecological research and management.     

Mapping an invasive bryophyte species using hyperspectral remote sensing data

Reliable distribution maps are crucial for the management of invasive plant species. An alternative to traditional field surveys is the use of remote sensing data, which allows coverage of large areas. However, most remote sensing studies on invasive plant species focus on mapping large stands of easily detectable study species. In this study, we used hyperspectral remote sensing data in combination with field data to derive a distribution map of an invasive bryophyte species, Campylopus introflexus, on the island of Sylt in Northern Germany. We collected plant cover data on 57 plots to calibrate the model and presence/absence data of C. introflexus on another 150 plots for independent validation. We simultaneously acquired airborne hyperspectral (APEX) images during summer 2014, providing 285 spectral bands. We used a Maxent modelling approach to map the distribution of C. introflexus. Although C. introflexus is a small and inconspicuous species, we were able to map its distribution with an overall accuracy of 75 %. Reducing the sampling effort from 57 to 7 plots, our models performed fairly well until sampling effort dropped below 12 plots. The model predicts that C. introflexus is present in about one quarter of the pixels in our study area. The highest percentage of C. introflexus is predicted in the dune grassland. Our findings suggest that hyperspectral remote sensing data have the potential to provide reliable information about the degree of bryophyte invasion, and thus provide an alternative to traditional field mapping approaches over large areas.     

元江-李仙江流域亚洲象生境适宜性评价——基于荟萃分析和遥感大数据分析

生境适宜性评价是物种保护和生境管理与规划的基础。近几十年来,云南境内野生亚洲象数量剧增,外扩迁移事件频发,而新迁入区域生境适宜状况因物种出现点数据缺乏而难以评价,掣肘迁移亚洲象保护与风险防范应急。以亚洲象新近迁入的元江-李仙江流域为案例区,采用荟萃分析统计亚洲象生境评价因子,结合相关性分析和方差膨胀因子独立性检验,筛选出生境评价因子;基于开源遥感数据产品量化生境因子,综合主成分分析和层次分析计算生境评价因子权重,采用生境适宜性指数(Habitat Suitability Index, HSI)模型评价元江-李仙江流域亚洲象生境适宜性,并分析其景观格局。结果表明：(1)元江-李仙江流域亚洲象生境适宜性空间格局主要表现为由下游至上游呈递减趋势,最适生境主要分布于流域下游段,而流域上游段适宜生境少;(2)元江流域生境适宜性低于李仙江流域,且其生境斑块连接度更低、破碎化更严重;(3)2021年“北移象群”北迁沿程生境适宜性由西南向东北呈下降趋势。基于亚洲象生境适宜性评价结果,科学引导野生亚洲象迁入适宜生境区,以规避人象冲突,保障外迁亚洲象群及其活动区居民生命财产安全,服务于区域生物多样性保护与...     

A Presence-Only Model of Suitable Roosting Habitat for the Endangered Indiana Bat in the Southern Appalachians

We know little about how forest bats, which are cryptic and mobile, use roosts on a landscape scale. For widely distributed species like the endangered Indiana bat Myotis sodalis, identifying landscape-scale roost habitat associations will be important for managing the species in different regions where it occurs. For example, in the southern Appalachian Mountains, USA, M. sodalis roosts are scattered across a heavily forested landscape, which makes protecting individual roosts impractical during large-scale management activities. We created a predictive spatial model of summer roosting habitat to identify important predictors using the presence-only modeling program MaxEnt and an information theoretic approach for model comparison. Two of 26 candidate models together accounted for >0.93 of AICc weights. Elevation and forest type were top predictors of presence; aspect north/south and distance-to-ridge were also important. The final average best model indicated that 5% of the study area was suitable habitat and 0.5% was optimal. This model matched our field observations that, in the southern Appalachian Mountains, optimal roosting habitat for M. sodalis is near the ridge top in south-facing mixed pine-hardwood forests at elevations from 260–575 m. Our findings, coupled with data from other studies, suggest M. sodalis is flexible in roost habitat selection across different ecoregions with varying topography and land use patterns. We caution that, while mature pine-hardwood forests are important now, specific areas of suitable and optimal habitat will change over time. Combining the information theoretic approach with presence-only models makes it possible to develop landscape-scale habitat suitability maps for forest bats.     

基于多源遥感的贵州草海国家级自然保护区黑颈鹤生境适宜性评价

生境适宜性评价是保护和管理濒危物种的重要途径。已有研究中用于物种生境适宜性评价的环境变量数据多存在分辨率低精度不高的问题,在研究小尺度物种生境适宜性时误差较大。为解决这一问题,本文根据黑颈鹤的出现点数据与光学、雷达遥感数据及地形辅助数据得出栖息地与觅食地特征,利用最大熵(MaxEnt)模型对草海越冬黑颈鹤细尺度的生境适宜性进行评价。结果显示：(1)运用多源遥感和地形辅助数据生成的环境变量结合MaxEnt预测黑颈鹤的栖息地与觅食地效果都较为优秀,两者受使用者工作特征曲线下的面积值(AUC)值均大于0.94;(2)距耕地距离、距水域距离、水深及距建筑距离是影响黑颈鹤栖息地主要环境因子,而影响觅食地分布的主要环境因子则是距耕地距离、优势植被、距建筑物距离和水深。(3)草海自然保护区黑颈鹤栖息地与觅食地的最适宜区和次适宜区面积较小,栖息地与觅食地最、次适宜区总和分别为6.404km~2与12.644 km~2,占比仅为研究区的6.43%和12.69%。通过调查发现,当前自然保护区的人为干扰源主要是游客和当地的居民,潜在地威胁着黑颈鹤的栖息地和觅食地。因此,为了避免保护区黑颈鹤栖息地与觅食地的退...     

Habitat suitability modelling for species at risk is sensitive to algorithm and scale: A case study of Blanding's turtle,Emydoidea blandingii,in Ontario,Canada

Species distribution modelling (SDM) can help conservation by providing information on the ecological requirements of species at risk. We developed habitat suitability models at multiple spatial scales for a threatened freshwater turtle, Emydoidea blandingii, in Ontario as a case study. We also explored the effect of background data selection and modelling algorithm selection on habitat suitability predictions. We used sighting records, high-resolution land cover data (25 m), and two SDM techniques: boosted regression trees; and maximum entropy modelling. The area under the receiver characteristic operating curve (AUC) for habitat suitability models tested on independent data ranged from 0.878 to 0.912 when using random background and from 0.727 to 0.741 with target-group background. E. blandingii habitat suitability was best predicted by air temperature, wetland area, open water area, road density, and cropland area. Habitat suitability increased with increasing air temperature and wetland area, and decreased with increasing cropland area. Low road density and open water increased habitat suitability, while high levels of either variable decreased habitat suitability. Robust habitat suitability maps for species at risk require using a multi-scale and multi-algorithm approach. If well used, SDM can offer insight on the habitat requirements of species at risk and help guide the development of management plans. Our results suggest that E. blandingii management plans should promote the protection of terrestrial habitat surrounding residential wetlands, halt the building of roads within and adjacent to currently occupied habitat, and identify movement corridors for isolated populations.     

Dealing with non-equilibrium bias and survey effort in presence-only invasive Species Distribution Models (iSDM); predicting the range of muntjac deer in Britain and Ireland

Invasive species managers utilise species records to inform management. These data can also be used in Species Distribution Models (SDM) to predict future spread or potential invasion of new areas. However, issues with non-equilibrium (also called disequilibrium) can cause difficulties in modelling invasive species that have not fully colonised their potential distribution and, in addition, sampling bias can result from a lack of information on survey effort, a particular issue for presence only modelling techniques. Geographical confounds are unavoidable when building iSDMs but there are methods that allow prediction to be optimised. We used maximum entropy (Maxent) to model suitable habitat for invasive Reeve's muntjac deer (Muntiacus reevesi) throughout Great Britain and Ireland comparing several methods that aimed to address invasive Species Distribution Modelling (iSDM) bias including spatial filtering, weighted background points and targeted background points built at varying spatial extents. Model evaluation metrics suggested that the model, which explicitly failed to account for non-equilibrium at the full extent of Great Britain and Ireland using random background points, predicted the species' current invasive range best. This highlighted that negative environmental relationships are likely to represent uncolonised areas rather than habitat selection and thus, low predicted suitability of uncolonised areas was misleading. Of the models that dealt with non-equilibrium conceptually best, by restricting the training extent to their current invasive range or core range, and utilised targeted background points accounting for survey effort (cells with other deer species recorded as present yet with no records for muntjac) as the best model evaluation metric, yielded relatively poor predictive performance. This implied limited habitat selectivity or avoidance within the colonised range which, when spatially extrapolated, suggested virtually all regions in Great Britain and Ireland may be vulnerable to future muntjac invasion.     

遥感用于森林生物多样性监测的进展

随着物种和栖息地的丧失,全球范围的生物多样性保护已经成为迫切的需要。航空航天技术的迅猛发展使遥感成为能提供跨越不同时空尺度监测陆地生态系统生物多样性的重要工具,这方面的研究在欧美等国已经有了小范围的开展,在国内刚刚起步。国外关于生物多样性遥感探测的方法基本有3种:1.利用遥感数据直接对物种或生境制图,进而估算生物多样性;2 .建立遥感数据的光谱反射率与地面观测物种多样性的关系模型;3.与野外调查数据结合直接在遥感数据上进行生物多样性指数制图。研究表明,物种直接制图法只能应用于较小的范围;生境制图的方法,应用广泛,技术相对成熟,研究范围局限于几百公里的范畴,但不能获取生境内部的多样性信息。光谱模型技术目前正处于探索阶段,对于植被复杂、生物多样性高的地域,具有较大的应用潜力。在遥感数据上直接进行生物多样性制图在加拿大已经得到了应用。     

Remote sensing of biodiversity: what to measure and monitor from space to species?

Global biodiversity monitoring systems through remote sensing can support consistent assessment, monitoring, modelling and reporting on biodiversity which are key activities intended for sustainable management. This work presents an overview of biodiversity monitoring components, i.e. biodiversity levels, essential biodiversity variables, biodiversity indicators, scale, biodiversity inventory, biodiversity models, habitat, ecosystem services, vegetation health and biogeochemical heterogeneity and discusses what remote sensing through Earth Observations has contributed to the study of biodiversity. The technological advancements in remote sensing have enabled information-rich data on biodiversity. Remote sensing data are making a strong contribution in providing unique information relevant to various biodiversity research and conservation applications. The extensive use of Earth observation data are not yet realized in biodiversity assessment, monitoring and conservation. The development of direct remote sensing approaches and the techniques for quantifying biodiversity at the community to species level is likely to be a great challenge for comprehensive earth observation-based monitoring strategy.

     

Habitat invasion risk assessment based on Landsat 5 data,exemplified by the shrub Rosa rubiginosa in southern Argentina

Prediction of invasive species spread helps to plan management actions. We performed a risk assessment by quantifying habitat invasibility, predicted the potential distribution of an invasive species using the Maxent modelling program and confirmed patterns using detailed field studies. Our study was conducted in southern Argentina, Patagonia, where large areas are already invaded by the European shrub Rosa rubiginosa. A total of 163 R. rubiginosa locations served as ground truth data, and predictors were obtained from the spaceborne sensor Landsat 5. Based on the Maxent Method (area under the receiver operating characteristic curve 0.8), the habitat invasibility map covered about 5000 km². Our model revealed that R. rubiginosa has the potential to invade 36% of the area along a steep precipitation gradient (target region 600–1400 mm per year). The Tasseled Cap brightness index and the normalized vegetation index explained most of the variance in our model, followed by the Tasseled Cap greenness and wetness indices, which can be interpreted as indicators of disturbance. Highest levels of invasibility were predicted for urban areas, along roads and rivers, on pastures, in Austrocedrus chilensis forests and inside Nothofagus dombeyi forest gaps. Detailed field assessments of rose cover performed in seven habitat types supported these results: rose cover significantly decreased with increasing tree cover (P < 0.01). Our data revealed that the occurrence of R. rubiginosa is not connected to a certain habitat type, but that it thrives in open patches following habitat disturbance. Our approach is a widely applicable, cost‐free remote sensing method that can serve as a risk assessment tool for alien plant species invasion of habitats.     

Benefits of hyperspectral remote sensing for tracking plant invasions

Aim We aim to report what hyperspectral remote sensing can offer for invasion ecologists and review recent progress made in plant invasion research using hyperspectral remote sensing. Location United States. Methods We review the utility of hyperspectral remote sensing for detecting, mapping and predicting the spatial spread of invasive species. We cover a range of topics including the trade‐off between spatial and spectral resolutions and classification accuracy, the benefits of using time series to incorporate phenology in mapping species distribution, the potential of biochemical and physiological properties in hyperspectral spectral reflectance for tracking ecosystem changes caused by invasions, and the capacity of hyperspectral data as a valuable input for quantitative models developed for assessing the future spread of invasive species. Results Hyperspectral remote sensing holds great promise for invasion research. Spectral information provided by hyperspectral sensors can detect invaders at the species level across a range of community and ecosystem types. Furthermore, hyperspectral data can be used to assess habitat suitability and model the future spread of invasive species, thus providing timely information for invasion risk analysis. Main conclusions Our review suggests that hyperspectral remote sensing can effectively provide a baseline of invasive species distributions for future monitoring and control efforts. Furthermore, information on the spatial distribution of invasive species can help land managers to make long‐term constructive conservation plans for protecting and maintaining natural ecosystems.     

Using range filling rather than prevalence of invasive plant species for management prioritisation: the case of Spathodea campanulata in the Society Islands (South Pacific)

Biological invasion science lacks standardised measures of invasion success that would provide effective prioritisation of invasive species and invaded areas management. Prevalence (area of occupancy) of invasive species is often used as proxy of their success but this metric ignores the extent to which a species fills its potential distribution. This study aims to estimate the performance of invasive tree species by computing the ratio between the compressed canopy area (CCA), assessed through remote sensing, and their potential distribution, estimated using invasive species distribution modelling. This index of ‘range filling’ (RF) has applicability to a broad set of invasive plant species in any biome. A case study is provided using the invasive African tulip tree Spathodea campanulata (Bignoniaceae) on three small tropical oceanic islands (South Pacific) exhibiting different invasion levels to test for differences between CCA and RF. The results show that the RF of Spathodea campanulata varied within islands depending on elevation but not proportionally to the CCA of the species. Another key result was that the RF of the species and its CCA provided different between-island perspectives on the invasions and lead to distinct ranking among islands to prioritise for management. Therefore, managers should disregard species’ prevalence as a measure of success and rather weight it with potential distribution to quantify how an invader is performing in a given environment.     

High resolution habitat suitability modelling for an endemic restricted-range Hawaiian insect (<Emphasis Type="Italic">Nysius wekiuicola</Emphasis>, Hemiptera: Lygaeidae)

Determining potentially suitable habitat is critical for effective species conservation and management, but can be challenging in remote or sensitive areas. An approach that combines non-intrusive spatial data collection techniques and supporting field data can lead to a better understanding of landscape-scale species distributions. Here we present two habitat suitability models, at 1 and 10 m resolutions, for the endemic wēkiu bug Nysius wekiuicola, a poorly-understood resident scavenging arthropod species present on the summit of Maunakea in Hawai‘i. Our models reveal that the wēkiu bug, restricted almost entirely to portions of cinder cones above 3500 m elevation, has a high degree of habitat specificity and represents a classically rare species. Across the 55 km² study area, 850 ha of potentially suitable habitat were identified at the 10 percentile training threshold, with the core area located at the true summit. Our results show that elevation and surficial mineralogy were the strongest predictors of suitable habitat, with lesser contributions from aspect and slope. Climatic variables also likely influence wēkiu bug distribution patterns, but were not included in our models due to the coarseness of available climate data and high correlation between variables. Relatively minor differences between the two models, in terms of identifying the locations and amount of suitable wēkiu bug habitat, and a higher measure of performance for the 10 m resolution model, suggest that coarser resolution input variables may characterize suitable habitat more efficiently than very fine 1 m resolution data. The suitability models generated as a result of this study will be directly incorporated into conservation management and restoration goals, and can easily be adapted for other arthropod species, leading to a more holistic understanding of metacommunity dynamics at the Maunakea summit.     

松嫩平原苍鹭秋季栖息地选择及适宜性分布

苍鹭（Ardea cinerea）是松嫩平原湿地的常见鸟种,松嫩平原也是苍鹭重要的栖息地。为了了解苍鹭潜在栖息地的适宜性分布,利用GPS/GSM卫星跟踪技术,结合遥感影像和地理信息系统,应用Maxent模型对松嫩平原苍鹭秋季潜在的栖息地进行了评价,并对其适宜性分布进行了分析。结果显示：水源距离和绿度指数是影响松嫩平原苍鹭秋季栖息地适宜性的重要环境变量;松嫩平原内苍鹭适宜栖息地面积为2761.06 km²（占研究区域的1.24%）,主要分布在大庆（756.86 km²,占适宜栖息地面积的27.41%）、白城（537.14 km²,占适宜栖息地面积的19.45%）、齐齐哈尔（439.43 km²,占适宜栖息地面积的15.92%）等地市行政区,以大庆市杜尔伯特蒙古族自治县（429.90 km²,占适宜栖息地面积的15.57%）、白城市镇赉县（334.92 km²,占适宜栖息地面积的12.13%）、大庆市肇源县（185.54 km²,占适宜栖息地面积的6.72%）等县级行政区为主;其中,15.79%的适宜栖息地依次受到莫莫格保护区（10.34%）、扎龙保护区（3.47%）、向海保护区（0.67%）、查干湖保护区（0.54%）、大布苏保护区（0.41%）、乌裕尔河保护区（0.36%）等国家级自然保护区的保护。建议对未受到保护的零星小面积栖息地给与更多关注。     

Mapping migratory bird prevalence using remote sensing data fusion

Background

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA.

Methodology and Principal Findings

A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species.

Conclusion and Significance

Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.     

普洱季风常绿阔叶林乔木物种多样性高分辨率遥感估测

季风常绿阔叶林是我国南亚热带典型的地带性植被,也是云南省普洱地区重要森林类型。季风常绿阔叶林乔木物种多样性遥感估测对研究区域尺度生物多样性格局及其规律具有重要作用。根据光谱异质性假说和环境异质性假说,首先使用1m空间分辨率的机载高光谱数据和激光雷达数据提取了光谱多样性特征和垂直结构特征。然后利用基于随机森林算法的递归特征消除方法选择对研究区森林乔木物种多样性指数具有较好解释能力的遥感特征,并对Shannon-Winner物种多样性指数进行建模、制图。研究结果表明：(1)基于机载LiDAR数据提取的垂直结构特征和机载高光谱数据提取的光谱多样性特征均对研究区森林乔木物种多样性具有较好的解释能力,随机森林模型估测结果分别为R²=0.48,RMSE=0.46和R²=0.5,RMSE=0.45;两种数据源融合可以进一步提高遥感数据的森林乔木物种多样性估测精度,随机森林估测模型R²和RMSE分别为0.69和0.37。(2)机载激光雷达数据对研究区针阔混交林乔木物种多样性的估测能力优于机载高光谱数据。(3)机器学习方法有助于从高维遥感...     

Application of ecological-niche factor analysis in habitat assessment of giant pandas

Ecological-niche factor analysis (ENFA) is a multivariate approach to study geographic distribution of species on a large scale with only “presence” data. It has been widely applied in many fields including wildlife management, habitat assessment and habitat prediction. In this paper, this approach was applied in habitat suitability assessment for giant pandas in Pingwu County, Sichuan Province, China. With “presence” data of giant pandas and remote sensing data, habitat suitability of pandas in this county was evaluated based on ENFA model, and spatial distribution pattern of nature reserves and conservation gaps were then evaluated. The results show that giant pandas in this county prefer high-elevation zones (> 2128 m) dominated by coniferous forest, and mixed coniferous and deciduous broadleaf forest, and avoid deciduous broadleaf forest and shrubs. Pandas avoid staying at habitats with human disturbances. Farmland is a major factor threatening panda habitat. Panda habitat is mainly distributed in north and west of Pingwu with a total area of 234033 hm², 106345hm² for suitable habitat and 127688 hm² for marginally suitable habitat). 3 nature reserves were located in Pingwu, covering over 49.2% of total suitable habitat and 45.6% of total marginally suitable habitat. Although 47.2% of panda habitat was in reserves under protection, connectivity between reserves was weak and a conservation gap existed in the north part of Pingwu. Thus, a new nature reserve in Baima and Mupi should be established to link the isolated habitats.     

The grass may not always be greener: projected reductions in climatic suitability for exotic grasses under future climates in Australia

Climate change presents a new challenge for the management of invasive exotic species that threaten both biodiversity and agricultural productivity. The invasion of exotic perennial grasses throughout the globe is particularly problematic given their impacts on a broad range of native plant communities and livelihoods. As the climate continues to change, pre-emptive long-term management strategies for exotic grasses will become increasingly important. Using species distribution modelling we investigated potential changes to the location of climatically suitable habitat for some exotic perennial grass species currently in Australia, under a range of future climate scenarios for the decade centred around 2050. We focus on eleven species shortlisted or declared as the Weeds of National Significance or Alert List species in Australia, which have also become successful invaders in other parts of the world. Our results indicate that the extent of climatically suitable habitat available for all of the exotic grasses modelled is projected to decrease under climate scenarios for 2050. This reduction is most severe for the three species of Needle Grass (genus Nassella) that currently have infestations in the south-east of the continent. Combined with information on other aspects of establishment risk (e.g. demographic rates, human-use, propagule pressure), predictions of reduced climatic suitability provide justification for re-assessing which weeds are prioritised for intensive management as the climate changes.     

Silvicolous on a Small Scale: Possibilities and Limitations of Habitat Suitability Models for Small,Elusive Mammals in Conservation Management and Landscape Planning

Species distribution and endangerment can be assessed by habitat-suitability modelling. This study addresses methodical aspects of habitat suitability modelling and includes an application example in actual species conservation and landscape planning. Models using species presence-absence data are preferable to presence-only models. In contrast to species presence data, absences are rarely recorded. Therefore, many studies generate pseudo-absence data for modelling. However, in this study model quality was higher with null samples collected in the field. Next to species data the choice of landscape data is crucial for suitability modelling. Landscape data with high resolution and ecological relevance for the study species improve model reliability and quality for small elusive mammals like Muscardinus avellanarius. For large scale assessment of species distribution, models with low-detailed data are sufficient. For regional site-specific conservation issues like a conflict-free site for new wind turbines, high-detailed regional models are needed. Even though the overlap with optimally suitable habitat for M. avellanarius was low, the installation of wind plants can pose a threat due to habitat loss and fragmentation. To conclude, modellers should clearly state the purpose of their models and choose the according level of detail for species and environmental data.     

Climate change increases risk of plant invasion in the Eastern United States

Invasive plant species threaten native ecosystems, natural resources, and managed lands worldwide. Climate change may increase risk from invasive plant species as favorable climate conditions allow invaders to expand into new ranges. Here, we use bioclimatic envelope modeling to assess current climatic habitat, or lands climatically suitable for invasion, for three of the most dominant and aggressive invasive plants in the southeast United States: kudzu (Pueraria lobata), privet (Ligustrum sinense; L. vulgare), and cogongrass (Imperata cylindrica). We define climatic habitat using both the Maxent and Mahalanobis distance methodologies, and we define the best climatic predictors based on variables that best ‘constrain’ species distributions and variables that ‘release’ the most land area if excluded. We then use an ensemble of 12 atmosphere-ocean general circulation models to project changes in climatic habitat for the three invasive species by 2100. The combined methodologies, predictors, and models produce a robust assessment of invasion risk inclusive of many of the approaches typically used individually to assess climate change impacts. Current invasion risk is widespread in southeastern states for all three species, although cogongrass invasion risk is more restricted to the Gulf Coast. Climate change is likely to enable all three species to greatly expand their ranges. Risk from privet and kudzu expands north into Ohio, Pennsylvania, New York, and New England states by 2100. Risk from cogongrass expands as far north as Kentucky and Virginia. Heightened surveillance and prompt eradication of small pockets of invasion in northern states should be a management priority.