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.     

Differences between the floras of the North and South Moluccas (Indonesia)

The north and south Moluccas (Indonesia) have very different geotectonic origins and, due to that, a difference in flora is to be expected. The north Moluccas moved westwards along the north coast of New Guinea to their present position, the south Moluccas moved north from Australia. On the other hand, a comparable climate in both areas and (partial) submergence during tectonic movement may have equalized both floras. Collection data from Naturalis Biodiversity Center on 1559 species in 121 families treated in Flora Malesiana were collected for the Moluccas, Sulawesi, and Western New Guinea (latitudes 9.2°S and 5.6°N and longitudes 118.8°E and 141°E) and georeferenced. Species Distribution Models (SDMs) were made, based on least correlated climate and edaphic variables, using only those species that were present in 5 or more grid cells of 5-arc minutes and models were tested for deviation from random. Both areas differ significantly and share only 50%-65% of their species. The 348 significant SDMs differ much less, though still significantly, sharing 91% of the species. Despite strong climatic and edaphic similarities between the North and South Moluccas, they differ greatly in species composition, which is in support of geotectonic reconstructions. The differences between the North and South Moluccas suggest that the continuous dispersal barriers and tectonic backgrounds have influenced their current flora.     

The potential range of <Emphasis Type="Italic">Ailanthus altissima</Emphasis> (tree of heaven) in South Africa: the roles of climate,land use and disturbance

Invasive trees are a major problem in South Africa. Many species are well established whereas others are still in the early stages of invasion. The management of invasive species is most cost effective at the early stages of invasion; it is thus essential to target and contain naturalizing invaders before they spread across the landscape. Multi-scale species distribution models (SDMs) provide useful insights to managers; they combine species-occurrence observations with climatic variables to predict potential distributions of alien species. Applying SDMs in human-dominated ecosystems is complicated because many factors associated with human actions interact in complex ways with climatic and edaphic factors to determine the potential suitability of sites for species. The aim of this study was to determine the degree to which a worldwide invader, A. altissima (Simaroubaceae) has occupied its potential range in South Africa, to identify areas at risk of future invasion. To do this we built a set of SDMs at both global and country scales using climatic, land use and human-footprint data. Climatic data best explained the distribution of A. altissima at the global scale whereas variables reflecting human-mediated disturbances were most influential at the national scale. Our analyses show the importance of human-mediated disturbances at a global scale and human occupancy at a country scale in determining the range limits of A. altissima. Populations of this tree species are already present in most parts of South Africa that are environmentally suitable for the species, and management actions need to focus on preventing increases in density in these areas.     

Incorporating biotic factors in species distribution modeling: are interactions with soil microbes important?

It is increasingly recognized that species distributions are driven by both abiotic factors and biotic interactions. Despite much recent work incorporating competition, predation, and mutualism into species distribution models (SDMs), the focus has been confined to aboveground macroscopic interactions. Biotic interactions between plants and soil microbial communities are understudied as potentially important drivers of plant distributions. Some soil bacteria promote plant growth by cycling nutrients, while others are pathogenic; thus they have a high potential for influencing plant occurrence. We investigated the influence of soil bacterial clades on the distributions of bryophytes and 12 vascular plant species in a high elevation talus‐field ecosystem in the Rocky Mountain Front Range, Colorado, USA. We used an information‐theoretic criterion (AICc) modeling approach to compare SDMs with the following different sets of predictors: abiotic variables, abiotic variables and other plant abundances, abiotic variables and soil bacteria clade relative abundances, and a full model with abiotic factors, plant abundances, and bacteria relative abundances. We predicted that bacteria would influence plant distributions both positively and negatively, and that these interactions would improve prediction of plant species distributions. We found that inclusion of either plant or bacteria biotic predictors generally improved the fit, deviance explained, and predictive power of the SDMs, and for the majority of the species, adding information on both other plants and bacteria yielded the best model. Interactions between the modeled species and biotic predictors were both positive and negative, suggesting the presence of competition, parasitism, and facilitation. While our results indicate that plant–plant co‐occurrences are a stronger driver of plant distributions than plant–bacteria co‐occurrences, they also show that bacteria can explain parts of plant distributions that remain unexplained by abiotic and plant predictors. Our results provide further support for including biotic factors in SDMs, and suggest that belowground factors be considered as well.     

中国植物分布模拟研究现状

在过去的20年里, 物种分布模型已广泛应用于动植物地理分布的模拟研究。该文以植物物种分布模拟为例, 利用中国知网、维普网以及Web of Science文献数据库的检索与统计, 分析了2000-2018年间, 中国研究人员利用各种物种分布模型对植物物种分布模拟研究的发文量、模拟模型、物种类型、数据来源、研究目的等信息。最终共收集到366篇有效文献, 分析表明2011年以来中国的物种分布模型应用发展迅速, 且以最近5年最为迅猛, 在生态学、中草药业、农业和林业等行业部门应用广泛。在使用的33种模型中, 应用最广的为最大熵模型(MaxEnt)。有一半研究的环境数据仅包含气候数据, 另一半研究不仅包含气候数据还包括地形与土壤等数据; 环境及物种数据的来源多样, 国际及本土数据库均得到使用。模拟涉及有明确清单的562个植物种, 既有木本植物(52.7%), 也有草本植物(41.8%), 其中中草药、果树、园林植物、农作物等占比较高。研究目的主要集中在过去、现在和未来气候变化对植物种分布的影响及预测, 以及物种分布评估与生物多样性评价(包括入侵植物风险评估)两大方面。预测物种潜在分布范围与气候变化影响等基础研究, 与模拟物种适生区与推广种植等应用研究并重, 物种分布模型在生态学与农业、林业和中草药业等多学科、多行业开展多种应用, 多物种、多模型和多来源数据共同参与模拟与比较, 开发新的机理性物种分布模型, 拓展新的物种分布模拟应用领域, 是今后研究的重点发展方向。     

Indicator plant species selection for monitoring the impact of climate change based on prediction uncertainty

To develop a long-term volunteer-based system for monitoring the impacts of climate change on plant distributions, potential indicator plants and monitoring sites were assessed considering habitat prediction uncertainty. We used species distribution models (SDMs) to project potential habitats for 19 popular edible wild plants in Japan. Prediction uncertainties of SDMs were assessed using three high-performance modeling algorithms and 19 simulated future climate data. SDMs were developed using presence/absence records, four climatic variables, and five non-climatic variables. The results showed that prediction uncertainties for future climate simulations were greater than those from the three different modeling algorithms. Among the 19 edible wild plant species, six had highly accurate SDMs and greater changes in occurrence probabilities between current and future climate conditions. The potential habitats of these six plants under future climate simulations tended to shift northward and upward, with predicted losses in potential southern habitats. These results suggest that these six plants are candidate indicators for long-term biological monitoring of the impacts of climate change. If temperature continuously increases as predicted, natural populations of these plants will decline in Kyushu, Chugoku and Shikoku districts, and in low altitudes of Chubu and Tohoku districts. These results also indicate the importance of occurrence probability and prediction uncertainty of SDMs for selecting target species and site locations for monitoring programs. Sasa kurilensis, a very popular and widespread dominant scrub bamboo in the cool-temperate regions of Japan, was found to be the most effective plant for monitoring.     

Incorporating intraspecific variation into species distribution models improves distribution predictions,but cannot predict species traits for a wide-spread plant species

The most common approach to predicting how species ranges and ecological functions will shift with climate change is to construct correlative species distribution models (SDMs). These models use a species’ climatic distribution to determine currently suitable areas for the species and project its potential distribution under future climate scenarios. A core, rarely tested, assumption of SDMs is that all populations will respond equivalently to climate. Few studies have examined this assumption, and those that have rarely dissect the reasons for intraspecific differences. Focusing on the arctic-alpine cushion plant Silene acaulis, we compared predictive accuracy from SDMs constructed using the species’ full global distribution with composite predictions from separate SDMs constructed using subpopulations defined either by genetic or habitat differences. This is one of the first studies to compare multiple ways of constructing intraspecific-level SDMs with a species-level SDM. We also examine the contested relationship between relative probability of occurrence and species performance or ecological function, testing if SDM output can predict individual performance (plant size) and biotic interactions (facilitation). We found that both genetic- and habitat-informed SDMs are considerably more accurate than a species-level SDM, and that the genetic model substantially differs from and outperforms the habitat model. While SDMs have been used to infer population performance and possibly even biotic interactions, in our system these relationships were extremely weak. Our results indicate that individual subpopulations may respond differently to climate, although we discuss and explore several alternative explanations for the superior performance of intraspecific-level SDMs. We emphasize the need to carefully examine how to best define intraspecific-level SDMs as well as how potential genetic, environmental, or sampling variation within species ranges can critically affect SDM predictions. We urge caution in inferring population performance or biotic interactions from SDM predictions, as these often-assumed relationships are not supported in our study.     

Botanical richness and endemicity patterns of Borneo derived from species distribution models

This study provides a Borneo-wide, quantitative assessment of botanical richness and endemicity at a high spatial resolution, and based on actual collection data. To overcome the bias in collection effort, and to be able to predict the presence and absence of species, even for areas where no collections have been made, we constructed species distribution models (SDMs) for all species taxonomically revised in Flora Malesiana. Species richness and endemicity maps were based on 1439 significant SDMs. Mapping of the residuals of the richness-endemicity relationship identified areas with higher levels of endemicity than can be expected on the basis of species richness, the endemicity hotspots. We were able to identify one previously unknown region of high diversity, the high mountain peaks of East Kalimantan; and two additional endemicity hotspots, the Müller Mountains and the Sangkulirang peninsula. The areas of high diversity and endemicity were characterized by a relatively small range in annual temperature, but with seasonality in temperatures within that range. Furthermore, these areas were least affected by El Niño Southern Oscillation drought events. The endemicity hotspots were found in areas, which were ecologically distinct in altitude, edaphic conditions, annual precipitation, or a combination of these factors. These results can be used to guide conservation efforts of the highly threatened forests of Borneo.     

Edaphic factors and plant species distribution in a protected area in the desert of Bahrain Island

Effects of edaphic factors (salinity, pH, Na⁺, K⁺, Ca⁺⁺, CaCO₃, water holding capacity, and grain size) on the spatial distribution of plants were investigated. Soil was sampled at 22 stands. Sixteen plant species were recorded from these stands. Relation between edaphic factors and plant distribution was investigated using correlation statistical analysis. Distribution of some plants was found to be highly correlated with edaphic factor(s).     

Changes in ectomycorrhizal fungal community composition and declining diversity along a 2‐million‐year soil chronosequence

Ectomycorrhizal (ECM) fungal communities covary with host plant communities along soil fertility gradients, yet it is unclear whether this reflects changes in host composition, fungal edaphic specialization or priority effects during fungal community establishment. We grew two co‐occurring ECM plant species (to control for host identity) in soils collected along a 2‐million‐year chronosequence representing a strong soil fertility gradient and used soil manipulations to disentangle the effects of edaphic properties from those due to fungal inoculum. Ectomycorrhizal fungal community composition changed and richness declined with increasing soil age; these changes were linked to pedogenesis‐driven shifts in edaphic properties, particularly pH and resin‐exchangeable and organic phosphorus. However, when differences in inoculum potential or soil abiotic properties among soil ages were removed while host identity was held constant, differences in ECM fungal communities and richness among chronosequence stages disappeared. Our results show that ECM fungal communities strongly vary during long‐term ecosystem development, even within the same hosts. However, these changes could not be attributed to short‐term fungal edaphic specialization or differences in fungal inoculum (i.e. density and composition) alone. Rather, they must reflect longer‐term ecosystem‐level feedback between soil, vegetation and ECM fungi during pedogenesis.     

Climate‐based empirical models show biased predictions of butterfly communities along environmental gradients

A better understanding of the factors that mould ecological community structure is required to accurately predict community composition and to anticipate threats to ecosystems due to global changes. We tested how well stacked climate‐based species distribution models (S‐SDMs) could predict butterfly communities in a mountain region. It has been suggested that climate is the main force driving butterfly distribution and community structure in mountain environments, and that, as a consequence, climate‐based S‐SDMs should yield unbiased predictions. In contrast to this expectation, at lower altitudes, climate‐based S‐SDMs overpredicted butterfly species richness at sites with low plant species richness and underpredicted species richness at sites with high plant species richness. According to two indices of composition accuracy, the Sorensen index and a matching coefficient considering both absences and presences, S‐SDMs were more accurate in plant‐rich grasslands. Butterflies display strong and often specialised trophic interactions with plants. At lower altitudes, where land use is more intense, considering climate alone without accounting for land use influences on grassland plant richness leads to erroneous predictions of butterfly presences and absences. In contrast, at higher altitudes, where climate is the main force filtering communities, there were fewer differences between observed and predicted butterfly richness. At high altitudes, even if stochastic processes decrease the accuracy of predictions of presence, climate‐based S‐SDMs are able to better filter out butterfly species that are unable to cope with severe climatic conditions, providing more accurate predictions of absences. Our results suggest that predictions should account for plants in disturbed habitats at lower altitudes but that stochastic processes and heterogeneity at high altitudes may limit prediction success of climate‐based S‐SDMs.     

Identifying the relevant spatial and temporal scales in plant species occurrence models: The case of arable weeds in landscape mosaic of crops

Species distribution models (SDMs) represent potential valuable tools to explore factors underlying species occurrence over a large range of spatial scales. However, a recurrent problem with this approach is identifying the appropriate spatial and temporal scales for modeling. This problem is reinforced in plant populations for which it is often difficult to evaluate the limits of habitat patches. In this study, we aimed at developing SDMs for 13 arable weeds in highly dynamic small agricultural region. Although weed dynamic is widely thought to result from local processes, we explored the spatial and temporal scales that would best explain species occurrence over the area. Models were developed using weed occurrence data in 58 fields over four consecutive years (2008–2011) and spatial organization of management practices over the landscape for eight consecutive years (2004–2011). We used a model selection approach based on the minimum AIC criteria to select the best SDMs. Results showed that SDMs can successfully be applied to model weed occurrence over a small region. The appropriate temporal scale to consider in weed SDMs should encompass several years to reflect the effect of management history while the relevant spatial scale should extend beyond the crop field itself and include the field border and neighboring fields. This study illustrates that adopting a multiple scale approach is successful to model plant occurrence over a highly dynamic landscape.     

Improving species distribution models for climate change studies: variable selection and scale

Statistical species distribution models (SDMs) are widely used to predict the potential changes in species distributions under climate change scenarios. We suggest that we need to revisit the conceptual framework and ecological assumptions on which the relationship between species distributions and environment is based. We present a simple conceptual framework to examine the selection of environmental predictors and data resolution scales. These vary widely in recent papers, with light inconsistently included in the models. Focusing on light as a necessary component of plant SDMs, we briefly review its dependence on aspect and slope and existing knowledge of its influence on plant distribution. Differences in light regimes between north‐ and south‐facing aspects in temperate latitudes can produce differences in temperature equivalent to moves 200 km polewards. Local topography may create refugia that are not recognized in many climate change SDMs using coarse‐scale data. We argue that current assumptions about the selection of predictors and data resolution need further testing. Application of these ideas can clarify many issues of scale, extent and choice of predictors, and potentially improve the use of SDMs for climate change modelling of biodiversity.     

Controlled comparison of species- and community-level models across novel climates and communities

Species distribution models (SDMs) assume species exist in isolation and do not influence one another''s distributions, thus potentially limiting their ability to predict biodiversity patterns. Community-level models (CLMs) capitalize on species co-occurrences to fit shared environmental responses of species and communities, and therefore may result in more robust and transferable models. Here, we conduct a controlled comparison of five paired SDMs and CLMs across changing climates, using palaeoclimatic simulations and fossil-pollen records of eastern North America for the past 21 000 years. Both SDMs and CLMs performed poorly when projected to time periods that are temporally distant and climatically dissimilar from those in which they were fit; however, CLMs generally outperformed SDMs in these instances, especially when models were fit with sparse calibration datasets. Additionally, CLMs did not over-fit training data, unlike SDMs. The expected emergence of novel climates presents a major forecasting challenge for all models, but CLMs may better rise to this challenge by borrowing information from co-occurring taxa.     

Does National Wetland Inventory class consistently identify vegetation and edaphic differences in Oregon tidal wetlands?

Accurately mapping, modeling, and managing the diversity of wetlands present in estuaries often relies on habitat classification systems that consistently identify differences in biotic structure or other ecosystem characteristics between classes. We used field data from four Oregon estuaries to test for differences in vegetation structure and edaphic characteristics among three tidal emergent marsh classes derived from National Wetlands Inventory (NWI) data: low estuarine marsh, high estuarine marsh, and tidal palustrine marsh. Independently of NWI class, we also evaluated the number and types of plant assemblages present and how edaphic variables, non-native plant cover, and plant species richness varied among them. Pore water salinity varied most strongly across marsh classes, with sediment carbon and nitrogen content, grain size and marsh surface elevation showing smaller differences. Cover of common vascular plant species differed between marsh classes and overall vegetation composition was somewhat distinct among marsh types. High estuarine marsh had the largest species pools. However, plot-level plant diversity was similar among marsh classes. Non-native species cover was highest in tidal palustrine and high estuarine marshes. The marshes in the study contained a large number of plant assemblages with most occurring across more than one marsh class. The more common assemblages occurred along a continuum of tidal elevation, soil salinity, and edaphic characteristics, with varying plant richness and non-native cover. Our data suggest that NWI classes are useful for differentiating several general features of Oregon tidal marsh structure, but that more detailed information on plant assemblages found within those wetland classes would allow more precise characterization of additional wetland features such as edaphic conditions and plant diversity.     

Effects of topographic and edaphic conditions on alpine plant species distribution along a slope gradient on Mount Norikura,central Japan

This study examined the effects of topographic and edaphic conditions on alpine plant species distribution along a slope gradient on Mt. Norikura (3026 m a.s.l.), central Japan. Topographic and edaphic factors investigated at 40 plots were: slope inclination, ground surface texture, soil water content and soil inorganic nitrogen concentration (NO₃-N, NH₄-N). The topographic and edaphic factors changed with slope positions: slope inclination was steeper, soil texture was coarser, and soil water and inorganic nitrogen concentration decreased with increasing slope position. Five vegetation types were located along the slope gradient and related to two factor-groups: (1) changes in soil water, NH₄-N, slope inclination along the slope gradient, and (2) ground surface texture. A tall herbaceous plant community developed at the low slope position, near tarns, with fine soil surface texture, high soil water and NH₄-N, while Dicentra peregrina dominated on an unstable rubble slope near the ridge top. The distribution of each species was predictable from the two factor-groups. Although the five vegetation types were related to the two factor-groups, responses to the two factor-groups differed among species, even within the same vegetation type. Therefore, this study showed that the topography of the terrain largely regulated alpine plant distribution by affecting edaphic conditions, and that global warming may alter species composition by changing edaphic conditions.     

Why georeferencing matters: Introducing a practical protocol to prepare species occurrence records for spatial analysis

Species Distribution Models (SDMs) are widely used to understand environmental controls on species’ ranges and to forecast species range shifts in response to climatic changes. The quality of input data is crucial determinant of the model's accuracy. While museum records can be useful sources of presence data for many species, they do not always include accurate geographic coordinates. Therefore, actual locations must be verified through the process of georeferencing. We present a practical, standardized manual georeferencing method (the Spatial Analysis Georeferencing Accuracy (SAGA) protocol) to classify the spatial resolution of museum records specifically for building improved SDMs. We used the high‐elevation plant Saxifraga austromontana Wiegand (Saxifragaceae) as a case study to test the effect of using this protocol when developing an SDM. In MAXENT, we generated and compared SDMs using a comprehensive occurrence dataset that had undergone three different levels of georeferencing: (1) trained using all publicly available herbarium records of the species, minus outliers (2) trained using herbarium records claimed to be previously georeferenced, and (3) trained using herbarium records that we have manually georeferenced to a ≤ 1‐km resolution using the SAGA protocol. Model predictions of suitable habitat for S. austromontana differed greatly depending on georeferencing level. The SDMs fitted with presence locations georeferenced using SAGA outperformed all others. Differences among models were exacerbated for future distribution predictions. Under rapid climate change, accurately forecasting the response of species becomes increasingly important. Failure to georeference location data and cull inaccurate samples leads to erroneous model output, limiting the utility of spatial analyses. We present a simple, standardized georeferencing method to be adopted by curators, ecologists, and modelers to improve the geographic accuracy of museum records and SDM predictions.     

Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges

Species distribution models (SDMs) use spatial environmental data to make inferences on species' range limits and habitat suitability. Conceptually, these models aim to determine and map components of a species' ecological niche through space and time, and they have become important tools in pure and applied ecology and evolutionary biology. Most approaches are correlative in that they statistically link spatial data to species distribution records. An alternative strategy is to explicitly incorporate the mechanistic links between the functional traits of organisms and their environments into SDMs. Here, we review how the principles of biophysical ecology can be used to link spatial data to the physiological responses and constraints of organisms. This provides a mechanistic view of the fundamental niche which can then be mapped to the landscape to infer range constraints. We show how physiologically based SDMs can be developed for different organisms in different environmental contexts. Mechanistic SDMs have different strengths and weaknesses to correlative approaches, and there are many exciting and unexplored prospects for integrating the two approaches. As physiological knowledge becomes better integrated into SDMs, we will make more robust predictions of range shifts in novel or non-equilibrium contexts such as invasions, translocations, climate change and evolutionary shifts.     

Exploring the spatiotemporal dynamics of habitat suitability to improve conservation management of a vulnerable plant species

Ongoing declines in biodiversity caused by global environmental changes call for adaptive conservation management, including the assessment of habitat suitability spatiotemporal dynamics potentially affecting species persistence. Remote sensing (RS) provides a wide-range of satellite-based environmental variables that can be fed into species distribution models (SDMs) to investigate species-environment relations and forecast responses to change. We address the spatiotemporal dynamics of species’ habitat suitability at the landscape level by combining multi-temporal RS data with SDMs for analysing inter-annual habitat suitability dynamics. We implemented this framework with a vulnerable plant species (Veronica micrantha), by combining SDMs with a time-series of RS-based metrics of vegetation functioning related to primary productivity, seasonality, phenology and actual evapotranspiration. Besides RS variables, predictors related to landscape structure, soils and wildfires were ranked and combined through multi-model inference (MMI). To assess recent dynamics, a habitat suitability time-series was generated through model hindcasting. MMI highlighted the strong predictive ability of RS variables related to primary productivity and water availability for explaining the test-species distribution, along with soil, wildfire regime and landscape composition. The habitat suitability time-series revealed the effects of short-term land cover changes and inter-annual variability in climatic conditions. Multi-temporal SDMs further improved predictions, benefiting from RS time-series. Overall, results emphasize the integration of landscape attributes related to function, composition and spatial configuration for improving the explanation of ecological patterns. Moreover, coupling SDMs with RS functional metrics may provide early-warnings of future environmental changes potentially impacting habitat suitability. Applications discussed include the improvement of biodiversity monitoring and conservation strategies.     

Specialized edaphic niches of threatened copper endemic plant species in the D.R. Congo: implications for ex situ conservation

Background and aims

Copper (Cu) rich soils derived from rocks of the Katangan Copperbelt in the south-eastern Democratic Republic of Congo (DRC) support a rich diversity of metallophytes including 550 heavy metal tolerant; 24 broad Cu soil endemic; and 33 strict Cu soil endemic plant species. The majority of the plant species occur on prominent Cu hills scattered along the copperbelt. Heavy metal mining on the Katangan Copperbelt has resulted in extensive degradation and destruction of the Cu hill ecosystems. As a result, approximately 80 % of the strict Cu endemic plant species are classified as threatened according to IUCN criteria and represent a conservation priority. Little is known about the soil Cu tolerance optimum of the Cu endemic plant species. The purpose of this study was to quantify the soil Cu concentration (Cu edaphic niche) of four Cu endemic plant species to inform soil propagation conditions and microhabitat site selection for planting of the species in Cu hill ecosystem restoration.

Methods

The soil Cu concentration tolerance of Cu endemic plant species was studied including Crotalaria cobalticola (CRCO); Gladiolus ledoctei (GLLE); Diplolophium marthozianum (DIMA); and Triumfetta welwitschii var. rogersii (TRWE-RO). The in situ natural habitat distributions of the Cu endemic plant species with respect to soil Cu concentration (Cu edaphic niche) was calculated by means of a generalised additive model. Additionally, the seedling emergence and growth of the four Cu endemic plant species in three soil Cu concentrations was tested ex situ and the results were compared to that of the natural habitat soil Cu concentration optimum (Cu edaphic niche).

Results

CRCO exhibited greater performance on the highest soil Cu concentration, consistent with its calculated Cu edaphic niche occurring at the highest soil Cu concentrations. In contrast, both DIMA and TRWE-RO exhibited greatest performance at the lowest soil Cu concentration, despite the calculated Cu edaphic niche occurring at moderate soil Cu concentrations. GLLE exhibited equal performances in the entire range of soil Cu concentrations.

Conclusions

These results suggest that CRCO evolved via the edaphic specialization model where it is most competitive in Cu hill habitat with the highest soil Cu concentration. In comparison, DIMA and TRWE-RO appear to have evolved via the endemism refuge model, which indicates that the species were excluded into (i.e., took refuge in) the lower plant competition Cu hill habitat due to their inability to effectively compete with higher plant competition on normal soils. The soil Cu edaphic niche determined for the four species will be useful in conservation activities including informing soil propagation conditions and microhabitat site selection for planting of the species in Cu hill ecosystem restoration.