Spatial modelling of ecological indicator values improves predictions of plant distributions in complex landscapes

Ecologically meaningful predictors are often neglected in plant distribution studies, resulting in incomplete niche quantification and low predictive power of species distribution models (SDMs). Because environmental data are rare and expensive to collect, and because their relationship with local climatic and topographic conditions are complex, mapping them over large geographic extents and at high spatial resolution remains a major challenge. Here, we propose to derive environmental data layers by mapping ecological indicator values in space. We combined ~6 million plant occurrences with expert-based plant ecological indicator values (EIVs) of 3600 species in Switzerland. EIVs representing local soil properties (pH, moisture, moisture variability, aeration, humus and nutrients) and climatic conditions (continentality, light) were modelled at 93 m spatial resolution with the Random Forest algorithm and 16 predictors representing meso-climate, land use, topography and geology. Models were evaluated and predictions of EIVs were compared with soil inventory data. We mapped each EIV separately and evaluated EIV importance in explaining the distribution of 500 plant species using SDMs with a set of 30 environmental predictors. Finally, we tested how they improve an ensemble of SDMs compared to a standard set of predictors for ca 60 plant species. All EIV models showed excellent performance (|r| > 0.9) and predictions were correlated reasonably (|r| > 0.4) to soil properties measured in the field. Resulting EIV maps were among the most important predictors in SDMs. Also, in ensemble SDMs overall predictive performance increased, mainly through improved model specificity reducing species range overestimation. Combining large citizen science databases to expert-based EIVs is a powerful and cost–effective approach for generalizing local edaphic and climatic conditions over large areas. Producing ecologically meaningful predictors is a first step for generating better predictions of species distribution which is of main importance for decision makers in conservation and environmental management projects.     

Testing the ability of species distribution models to infer variable importance

Models of species’ distributions and niches are frequently used to infer the importance of range- and niche-defining variables. However, the degree to which these models can reliably identify important variables and quantify their influence remains unknown. Here we use a series of simulations to explore how well models can 1) discriminate between variables with different influence and 2) calibrate the magnitude of influence relative to an ‘omniscient’ model. To quantify variable importance, we trained generalized additive models (GAMs), Maxent and boosted regression trees (BRTs) on simulated data and tested their sensitivity to permutations in each predictor. Importance was inferred by calculating the correlation between permuted and unpermuted predictions, and by comparing predictive accuracy of permuted and unpermuted predictions using AUC and the continuous Boyce index. In scenarios with one influential and one uninfluential variable, models failed to discriminate reliably between variables when training occurrences were < 8–64, prevalence was > 0.5, spatial extent was small, environmental data had coarse resolution and spatial autocorrelation was low, or when pairwise correlation between environmental variables was |r| > 0.7. When two variables influenced the distribution equally, importance was underestimated when species had narrow or intermediate niche breadth. Interactions between variables in how they shaped the niche did not affect inferences about their importance. When variables acted unequally, the effect of the stronger variable was overestimated. GAMs and Maxent discriminated between variables more reliably than BRTs, but no algorithm was consistently well-calibrated vis-à-vis the omniscient model. Algorithm-specific measures of importance like Maxent's change-in-gain metric were less robust than the permutation test. Overall, high predictive accuracy did not connote robust inferential capacity. As a result, requirements for reliably measuring variable importance are likely more stringent than for creating models with high predictive accuracy.     

PSDS 2.0: 一个基于GIS和多个模型的生物潜在分布地预测系统

根据对生物分布地预测模型和软件发展现状的分析和总结, 本研究在PSDS 1.0的基础上提出并实现一个基于GIS且具有多个代表性模型的生物分布地预测系统(PSDS 2.0)。PSDS 2.0系统继承了1.0的环境包络和聚类包络模型, 进一步引入了限制因子包络、马氏距离、支持向量机等新模型, 并针对本领域中模型比较与选择的难点增加了迭代交叉验证的多模型选择功能。系统还实现了灵活定制和评估伪负样本的功能, 通过用只需要正样本的I类模型预测的结果对随机产生的伪负样本进行评估, 减小其落入适宜地区的概率, 进一步提高需要正负样本的II类模型的准确率。GIS功能在PSDS 2.0中也得到加强, 被应用于数据准备及结果分析等重要环节。文章最后以白冠长尾雉(Syrmaticus reevesii)为例, 运用PSDS 2.0系统预测其在中国范围内的潜在分布地, 并对各种模型的预测结果进行评估和比较。     

A standard protocol for reporting species distribution models

Species distribution models (SDMs) constitute the most common class of models across ecology, evolution and conservation. The advent of ready-to-use software packages and increasing availability of digital geoinformation have considerably assisted the application of SDMs in the past decade, greatly enabling their broader use for informing conservation and management, and for quantifying impacts from global change. However, models must be fit for purpose, with all important aspects of their development and applications properly considered. Despite the widespread use of SDMs, standardisation and documentation of modelling protocols remain limited, which makes it hard to assess whether development steps are appropriate for end use. To address these issues, we propose a standard protocol for reporting SDMs, with an emphasis on describing how a study's objective is achieved through a series of modeling decisions. We call this the ODMAP (Overview, Data, Model, Assessment and Prediction) protocol, as its components reflect the main steps involved in building SDMs and other empirically-based biodiversity models. The ODMAP protocol serves two main purposes. First, it provides a checklist for authors, detailing key steps for model building and analyses, and thus represents a quick guide and generic workflow for modern SDMs. Second, it introduces a structured format for documenting and communicating the models, ensuring transparency and reproducibility, facilitating peer review and expert evaluation of model quality, as well as meta-analyses. We detail all elements of ODMAP, and explain how it can be used for different model objectives and applications, and how it complements efforts to store associated metadata and define modelling standards. We illustrate its utility by revisiting nine previously published case studies, and provide an interactive web-based application to facilitate its use. We plan to advance ODMAP by encouraging its further refinement and adoption by the scientific community.     

Invasive behaviour of Lactuca serriola (Asteraceae) in the Netherlands: Spatial distribution and ecological amplitude

Species invasions have been a central theme in ecology over the past decades, with a focus on invasions of non-indigenous European species in the New World. However, within Europe, native species may also become invasive. Such species rapidly increase their geographic range and may at the same time broaden their ecological amplitude. We report on Lactuca serriola L., which has shown a rapid range expansion in many parts of Europe. We reconstructed its geographical spread through The Netherlands; furthermore, we tested for changes in its ecological amplitude.

Before 1950, L. serriola was rare in The Netherlands, recorded from only 80 grid-cells (of 5×5 km²). Subsequently, its distribution increased from 219 grid-cells (1980), to 546 cells (990) and 998 grid cells (2000). It currently occupies at least 60% of The Netherlands. Analyses of relevées demonstrate, using detrended correspondence analysis (DCA), that the variance in the assemblage of species co-occurring with L. serriola increased significantly since 1940, indicating that the species currently occurs in a broader range of vegetation types. Correlating averages of Ellenberg's indicator values per vegetation relevée with time, we found correlations for continentality (+), acidity (−) and (soil) humidity (+). Moreover, the proportion of phanerophytes in the relevées increased over time. These findings suggest that in addition to its original ruderal habitat, L. serriola also currently occurs in more closed vegetation types. Based on analyses of two other co-occurring Asteraceae species, we showed that these patterns were specific for L. serriola only. We conclude that L. serriola has broadened its ecological amplitude and discuss four mutually non-exclusive explanations for the recent invasiveness of L. serriola: (i) Effects of a changed environment: global warming and ruderalisation; (ii) metapopulation dynamics and increased diaspore pressure; (iii) microevolution; (iv) genetic reinforcement due to hybridisation with conspecific (crop) species.     

一种模型模拟结果的统计检验方法

模拟结果的准确性是衡量生态学模型是否成功的关键,但采用统计学方法判别模型模拟结果与观察值相符程度的报道较少。根据两个直线回归方程能否合并为一个方程的统计学检验方法,提出了通过检验观察值与模拟值直线回归方程和1∶1直线方程截距与斜率是否相同,进而在统计显著水平上判断生态学模型模拟值与观察值一致性的统计学检验方法。数据检验表明,此方法可以较好解决判断生态学模型模拟结果准确性的问题。     

Abandonment of traditionally managed mesic mountain meadows affects plant species composition and diversity

In recent years abandonment of traditional management of mountain grasslands has been observed throughout Central Europe. However, the impact of abandonment on vegetation of mountain grasslands is still unclear. In this study it was hypothesized that the cessation of traditional management of mesic mountain meadows causes changes in their species composition and a decrease in the biodiversity. In total, 260 plots were established in the Sudetes (SW Poland) on meadows with regular annual mowing, meadows with irregular mowing management, and abandoned meadows. Relevés (5 × 5 m) were performed, and the habitat properties were determined using Ellenberg indicator values. The study confirmed the hypothesis that the various ways of extensive management have an influence on species richness. The lowest species richness was observed on the irregularly managed meadows, while higher species numbers were found on the abandoned and regular managed meadows. The majority of patches on abandoned meadows exhibited degradation through the expansion of Solidago gigantea, Solidago canadensis, Lupinus polyphyllus, Heracleum sosnovsky, Calamagrostis epigejos, Deschampsia flexuosa, Festuca rubra and Hypericum maculatum. Meadows subjected to different management practices differed significantly in Ellenberg indicator values. The abandoned meadows had the highest values of the light index (L) and nitrogen availability (N), whereas the highest values of soil moisture (F) were noted on the irregularly managed meadows. The degradation of mountain mesic meadows requires regular mowing management, which stops ecological succession and preserves their high biodiversity.     

Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?

Modelling strategies for predicting the potential impacts of climate change on the natural distribution of species have often focused on the characterization of a species’ bioclimate envelope. A number of recent critiques have questioned the validity of this approach by pointing to the many factors other than climate that play an important part in determining species distributions and the dynamics of distribution changes. Such factors include biotic interactions, evolutionary change and dispersal ability. This paper reviews and evaluates criticisms of bioclimate envelope models and discusses the implications of these criticisms for the different modelling strategies employed. It is proposed that, although the complexity of the natural system presents fundamental limits to predictive modelling, the bioclimate envelope approach can provide a useful first approximation as to the potentially dramatic impact of climate change on biodiversity. However, it is stressed that the spatial scale at which these models are applied is of fundamental importance, and that model results should not be interpreted without due consideration of the limitations involved. A hierarchical modelling framework is proposed through which some of these limitations can be addressed within a broader, scale‐dependent context.     

物种分布模型理论研究进展

利用物种分布模型估计物种的真实和潜在分布区,已成为区域生态学与生物地理学中非常活跃的研究领域。然而,到目前为止,这项技术的理论基础仍然存在不足之处,一些关键的生态过程未能被有效纳入到物种分布模型的理论框架中,从而为解释物种分布模型预测的结果带来了诸多困惑。鉴于此,总结了物种分布模型的理论基础;系统探讨了物种分布模型与物种分布区的关系;特别指出了物种分布模型研究中存在的理论问题;重点阐述了物种分布模型未来的发展方向。研究认为,物种分布模型与生态位理论、源-库理论、种群动态理论、集合种群理论、进化理论等具有重要的联系;正确理解物种分布模型的预测结果与物种分布区的关系,有赖于对影响物种分布的3个主要因素(环境条件、物种相互作用与物种迁移能力)做出定量的分离;目前物种分布模型主要存在的问题是未能将物种的相互作用和物种的迁移能力有效纳入到模型的构建过程中;未来物种分布模型的发展应该加强模型背后理论框架的研究,并进一步加强整合物种相互作用过程、种群动态过程、迁移过程和物种进化过程等内容。研究还认为,从更高的理论层次模拟功能群和群落结构将是未来物种分布模型的重要发展方向。     

提高生态位模型转移能力来模拟入侵物种 的潜在分布

生态位模型利用物种分布点所关联的环境变量去推算物种的生态需求, 模拟物种的分布。在模拟入侵物种分布时, 经典生态位模型包括模型构建于物种本土分布地, 然后将其转移并投射至另一地理区域, 来模拟入侵物种的潜在分布。然而在模型运用时, 出现了模型的转移能力较低、模拟的结果与物种的实际分布不相符的情况, 由此得出了生态位漂移等不恰当的结论。提高生态位模型的转移能力, 可以准确地模拟入侵物种的潜在分布, 为入侵种的风险评估提供参考。作者以入侵种茶翅蝽(Halyomorpha halys)和互花米草(Spartina alterniflora)为例, 从模型的构建材料(即物种分布点和环境变量)入手, 全面阐述提高模型转移能力的策略。在构建模型之前, 需要充分了解入侵物种的生物学特性、种群平衡状态、本土地理分布范围及物种的生物历史地理等方面的知识。在模型构建环节上, 物种分布点不仅要充分覆盖物种的地理分布和生态空间的范围, 同时要降低物种采样点偏差; 环境变量的选择要充分考虑其对物种分布的限制作用、各环境变量之间的空间相关性, 以及不同地理种群间生态空间是否一致, 同时要降低环境变量的空间维度; 模型构建区域要真实地反映物种的地理分布范围, 并考虑种群的平衡状态。作者认为, 在生态位保守的前提下, 如果模型是构建在一个合理方案的基础上, 生态位模型的转移能力是可以保证的, 在以模型转移能力较低的现象来阐述生态位分化时需要引起注意。     

Ecological niche and phylogeny explain distribution of seed mass in the central European flora

Seed size is a crucial life‐history trait determining the amount of reserves that are available to establishing seedlings. The most frequently observed patterns in seed size distribution are a higher frequency of large‐seeded species in shaded habitats and a positive correlation of seed size with plant size. We analysed to what extent realised niche dimensions, as expressed by Ellenberg indicator values and plant functional traits such as plant height and life form, explained seed mass variation in the central European flora. By including information on phylogenetic relatedness of the species, not only contemporary ecology but also the evolutionary history of plant species could be taken into account. Seed mass evolution was slow and was best explained by selection‐inertia models with multiple adaptive peaks as a function of either habitat or life form. The highest seed mass optima were observed in the deciduous forest and saltwater and seashore habitats, and in phanerophytes in case of models with optima as a function of life form. The analyses showed that Ellenberg values were more important than habitat and life form in explaining seed mass distribution in the central European flora. The often observed relation between shade and large seeds was also evident in our study, but we found an equally important relation between large seeds and drought and a positive relation between seed mass and salinity. Our results indicate that not only plant size and competition for light but a complex set of factors influence the ecology of seed size, and that a more precise delineation of species’ niches improves the understanding of seed size evolution.     

Confronting species distribution model predictions with species functional traits

Species distribution models are valuable tools in studies of biogeography, ecology, and climate change and have been used to inform conservation and ecosystem management. However, species distribution models typically incorporate only climatic variables and species presence data. Model development or validation rarely considers functional components of species traits or other types of biological data. We implemented a species distribution model (Maxent) to predict global climate habitat suitability for Grass Carp (Ctenopharyngodon idella). We then tested the relationship between the degree of climate habitat suitability predicted by Maxent and the individual growth rates of both wild (N = 17) and stocked (N = 51) Grass Carp populations using correlation analysis. The Grass Carp Maxent model accurately reflected the global occurrence data (AUC = 0.904). Observations of Grass Carp growth rate covered six continents and ranged from 0.19 to 20.1 g day⁻¹. Species distribution model predictions were correlated (r = 0.5, 95% CI (0.03, 0.79)) with observed growth rates for wild Grass Carp populations but were not correlated (r = −0.26, 95% CI (−0.5, 0.012)) with stocked populations. Further, a review of the literature indicates that the few studies for other species that have previously assessed the relationship between the degree of predicted climate habitat suitability and species functional traits have also discovered significant relationships. Thus, species distribution models may provide inferences beyond just where a species may occur, providing a useful tool to understand the linkage between species distributions and underlying biological mechanisms.     

Forest vascular plants as indicators of plant species richness: A data analysis of a flora atlas from northwestern Germany

Abstract

Our study had the objective to examine whether the number of forest vascular plants in a forest-poor region may be indicative of total plant species richness and of the number of threatened plant species. We also related forest plant species richness to geological and soil variables. The analysis was based on a regional flora atlas from the Weser-Elbe region in northwestern Germany including incidence data of species in a total of 1109 grid cells (each ca. 2.8 × 2.8 km²). All taxa were classified either as forest or non-forest species. Total species richness in the grid cells ranged from 65 to 597, with a mean value of 308. The number of forest species varied between 20 and 309 (mean 176). Grid cells with or without particular geological units differed in total and forest species richness, with those containing peatland and marshland being particularly species-poor. Indicator value analysis showed that both total and forest species richness in the grid cells were related to soil acidity and nitrogen in a hump-backed manner, with the highest number of species found at moderately low values for nitrogen and at intermediate values of pH. Forest species richness was highly positively correlated with the number of non-forest species and threatened non-forest species. Indicators for high species richness were primarily those species that are confined to closed semi-natural forests with a varied topography and relatively base- and nutrient-rich soils. Grid cells including historically ancient forest exhibited a higher species richness than grid cells lacking ancient forest, indicating the importance of a long habitat continuity for a high phytodiversity. The “habitat coincidence” of high species richness is best explained by similar responses of forest species and species of other habitats to the main environmental gradients. It is suggested that the regional patterns found for the Weser-Elbe region can be transferred also to other forest-poor regions in Central Europe.     

Predicting the global areas for potential distribution of Gastrodia elata based on ecological niche models

Aims Previous studies on the globally suitable areas for growing the medicinal plant Gastrodia elata is lacking. This study aims to predict the global areas for potential distribution of this plant based on multiple ecological niche models. Methods A total of 220 global distribution points of G. elata and 19 ecological variables were compiled and eight environmental variables were selected for the model training. Three ecological niche models, including BIOCLIM, DOMAIN, and MAXENT, were used to predict the global areas for potential distribution of G. elata. The resulting data of different models were analyzed and compared with two statistical criteria: the area under the receiver operating characteristic curve (AUC) and Kappa value. Important findings The predictions of the three models are basically identical, showing that the global areas for potential distribution of G. elata are predominantly in the range of 20° N to 50° N in Asia, mainly in China, South Korea and Japan. A small proportion of the suitable areas occur in India, Nepal and the European countries near Mediterranean. The most suitable areas distribute in provinces close to the Sichuan Basin and the central East China, the mid-eastern parts of South Korea such as Chungcheongbuk-do, Gyeongsangbuk-do and Gyeongsangnam- do, and the Kyushu region and the Shikoku region on Japan’s main island. Therefore, these three countries can be used as the main production areas of G. elata for its commercial development. The AUC average values of the three models are all above 0.9 and the Kappa average values all above 0.65, justifying their applications for predicting the potential areas of G. elata. Among them, the MAXENT model appears to perform the best, followed by DOMAIN and BIOCLIM.     

Possible interactions between environmental factors in determining species optima

Questions: 1. Indicator values, such as those of Ellenberg, for different environmental factors are seen as independent. We tested for the presence of interactions between environmental factors (soil moisture and reaction) to see if this assumption is simplistic. 2. How close are Ellenberg indicator values (IV s) related to the observed optima of species response curves in an area peripheral to those where they have been previously employed and 3. Can the inclusion of bryophytes add to the utility of IVs? Location: South Uist, Outer Hebrides, Scotland, UK. Methods: Two grids (ca. 2000 m × 2000 m) were sampled at 50‐m intervals across the transition from machair to upland communities covering an orthogonal gradient of both soil pH (reaction) and soil moisture content. Percentage cover data for vascular plants, bryophytes and lichens were recorded, along with pH and moisture content of the underlying sand/soil/ peat. Reaction optima, derived from species response curves calculated using HOF models, were compared between wet and dry sites, and moisture optima between acidic and basic samples. Optima for the whole data set were compared to Ellenberg IVs to assess their performance in this area, with and without the inclusion of bryophytes. Results: A number of species showed substantially different pH optima at high and low soil moisture contents (18% of those tested) and different soil moisture optima at high and low pH (49%). For a number of species the IVs were poor predictors of their actual distribution across the sampled area. Bryophytes were poor at explaining local variation in the environmental factors and also their inclusion with vascular plants negatively affected the strength of relationships. Conclusions: A substantial number of species showed an interaction between soil moisture and reaction in determining their optima on the two respective gradients. It should be borne in mind that IVs such as Ellenberg's may not be independent of one another.