Database records as a surrogate for sampling effort provide higher species richness estimations

The compilation of all the available taxonomic and distributional information on the species present in a territory frequently generates a biased picture of the distribution of biodiversity due to the uneven distribution of the sampling effort performed. Thus, quality protocol assessments such as those proposed by Hortal et al. (Conservation Biology 21:853–863, 2007) must be done before using this kind of information for basic and applied purposes. The discrimination of localities that can be considered relatively well-surveyed from those not surveyed enough is a key first step in this protocol and can be attained by the previous definition of a sampling effort surrogate and the calculation of survey completeness using different estimators. Recently it has been suggested that records from exhaustive databases can be used as a sampling-effort surrogate to recognize probable well-surveyed localities. In this paper, we use an Iberian dung beetle database to identify the 50 × 50 km UTM cells that appear to be reliably inventoried, using both data derived from standardized sampling protocols and database records as a surrogate for sampling effort. Observed and predicted species richness values in the shared cells defined as well-surveyed by both methods suggest that the use of database records provides higher species richness values, which are proportionally greater in the richest localities by the inclusion of rare species.     

Species richness coincidence: conservation strategies based on predictive modelling

The present-day geographic distribution of individual species of five taxonomic groups (plants, dragonflies, butterflies, herpetofauna and breeding birds) is relatively well-known on a small scale (5 × 5 km squares) in Flanders (north Belgium). These data allow identification of areas with a high diversity within each of the species groups. However, differences in mapping intensity and coverage hamper straightforward comparisons of species-rich areas among the taxonomic groups. To overcome this problem, we modelled the species richness of each taxonomic group separately using various environmental characteristics as predictor variables (area of different land use types, biotope diversity, topographic and climatic features). We applied forward stepwise multiple regression to build the models, using a subset of well-surveyed squares. A separate set of equally well-surveyed squares was used to test the predictions of the models. The coincidence of geographic areas with high predicted species richness was remarkably high among the four faunal groups, but much lower between plants and each of the four faunal groups. Thus, the four investigated faunal groups can be used as relatively good indicator taxa for one another in Flanders, at least for their within-group species diversity. A mean predicted species diversity per mapping square was also estimated by averaging the standardised predicted species richness over the five taxonomic groups, to locate the regions that were predicted as being the most species-rich for all five investigated taxonomic groups together. Finally, the applicability of predictive modelling in nature conservation policy both in Flanders and in other regions is discussed.     

Influence of geographic database scale on prediction of groundwater vulnerability to pesticides

Selection of optimal locations for sampling of groundwater is an important aspect of determining the fate of pesticides in the environment. For large land areas such as states and counties, the interaction of physical and chemical properties of soil, geologic strata, and pesticide molecule are quite complicated and highly variable. This article presents information that shows that the scale of the database influences not only the prediction of the vulnerability of ground‐water to pesticides, but also the areal coverage. In this study, the statewide agricultural pesticide in groundwater model was modified to evaluate the vulnerability of the uppermost aquifer in Woodruff County, AR, to pesticides. The state scale model used soil, geological, and topological databases on a 1:500,000 scale. In contrast, the county‐scale model used databases that were specific to the data layer rather than inferred and used soils at a 1:24,000 scale. A land use component was added to both models to reflect where pesticides are possibly applied in the county. The predictive ability of the two models was compared for nine wells previously sampled for pesticides. On the average, the county‐scale model had higher indices for the wells, indicating a greater vulnerability of groundwater to pesticides at these locations. At the well site where the highest concentration of a pesticide was found, the county‐scale model had a considerably higher vulnerability index.     

Assessing the accuracy of species distribution models to predict amphibian species richness patterns

1. Evaluating the distribution of species richness where biodiversity is high but has been insufficiently sampled is not an easy task. Species distribution modelling has become a useful approach for predicting their ranges, based on the relationships between species records and environmental variables. Overlapping predictions of individual distributions could be a useful strategy for obtaining estimates of species richness and composition in a region, but these estimates should be evaluated using a proper validation process, which compares the predicted richness values and composition with accurate data from independent sources. 2. In this study, we propose a simple approach to estimate model performance for several distributional predictions generated simultaneously. This approach is particularly suitable when species distribution modelling techniques that require only presence data are used. 3. The individual distributions for the 370 known amphibian species of Mexico were predicted using maxent to model data on their known presence (66,113 presence-only records). Distributions were subsequently overlapped to obtain a prediction of species richness. Accuracy was assessed by comparing the overall species richness values predicted for the region with observed and predicted values from 118 well-surveyed sites, each with an area of c. 100 km(2), which were identified using species accumulation curves and nonparametric estimators. 4. The derived models revealed a remarkable heterogeneity of species richness across the country, provided information about species composition per site and allowed us to obtain a measure of the spatial distribution of prediction errors. Examining the magnitude and location of model inaccuracies, as well as separately assessing errors of both commission and omission, highlights the inaccuracy of the predictions of species distribution models and the need to provide measures of uncertainty along with the model results. 5. The combination of a species distribution modelling method like maxent and species richness estimators offers a useful tool for identifying when the overall pattern provided by all model predictions might be representing the geographical patterns of species richness and composition, regardless of the particular quality or accuracy of the predictions for each individual species.     

Determining threatened species distributions in the face of limited data: Spatial conservation prioritization for the Chinese giant salamander (Andrias davidianus)

The purpose of this study was to determine whether limited occurrence data for highly threatened species can provide useful spatial information to inform conservation. The study was conducted across central and southern China. We developed a habitat suitability model for the Critically Endangered Chinese giant salamander (Andrias davidianus) based on one biotic and three abiotic parameters from single‐site locality records, which represent the only relevant environmental data available for this species. We then validated model quality by testing whether increased percentage of predicted suitable habitat at the county level correlated with independent data on giant salamander presence. We randomly selected 48 counties containing historical records which were distinct from, and independent of, the single‐site records used to develop the model, and 47 additional counties containing >50% predicted suitable habitat. We interviewed 2,812 respondents near potential giant salamander habitat across these counties and tested for differences in respondent giant salamander reports between counties selected using each method. Our model predicts that suitable giant salamander habitat is found widely across central and southern China, with counties containing ≥50% predicted suitable habitat distributed in 13 provinces. Counties with historical records contain significantly more predicted suitable habitat than counties without historical records. There are no statistical differences in any patterns of respondent giant salamander reports in surveyed counties selected from our model compared with the areas of known historical giant salamander distribution. A Chinese giant salamander habitat suitability model with strong predictive power can be derived from the restricted range of environmental variables associated with limited available presence‐only occurrence records, constituting a cost‐effective strategy to guide spatial allocation of conservation planning. Few reported sightings were recent, however, with most being over 20 years old, so that identification of areas of suitable habitat does not necessarily indicate continued survival of the species at these locations.     

Anthropogenic impacts upon plant species richness and net primary productivity in California

We assess the importance of anthropogenic land‐use, altered productivity, and species invasions for observed productivity–richness relationships in California. To this end, we model net primary productivity (NPP) c. 1750 AD and at present (1982–1999) and map native and exotic vascular plant richness for 230 subecoregions. NPP has increased up to 105% in semi‐arid areas and decreased up to 48% in coastal urbanized areas. Exotic invasions have increased local species diversity up to 15%. Human activities have reinforced historical gradients in species richness but reduced the spatial heterogeneity of NPP. Structural equation modelling suggests that, prior to European settlement, NPP and richness were primarily controlled by precipitation and other abiotic variables, with NPP mediating richness. Abiotic variables remain the strongest predictors of present NPP and richness, but intermodel comparisons indicate a significant anthropogenic impact upon statewide distributions of NPP and richness. Exotic and native species each positively correlate to NPP after controlling for other variables, which may help explain recent reports of positively associated native and exotic richness.     

中国生态农业模式管理信息及决策支持系统的建立

根据全国生态农业试点县建设和典型生态农业模式研究的经验,利用Access数据库技术建立了全国首批生态农业县有关自然资源背景、农业生产水平、生态环境与工程技术等各种基础信息数据库和所推广应用的生态农业模式信息数据库,可快速方便地提供各生态农业县相关信息或知识的查询或编辑．在此基础上,采用了面向对象的推理方法建立了生态农业模式区域决策的知识库体系模型,并利用Visual C^++语言初步开发出生态农业模式的区域决策支持系统,基本上实现了区域生态农业模式的决策推荐．     

Comparison of predictor sets for species richness and the number of rare species of butterflies and birds

Aim Accurate inventories of biota are typically restricted to few locations within an extensive region. Accordingly, effective planning must involve some form of surrogate measures coupled with spatial modelling. We conducted a simultaneous comparison of models of both species richness and the number of rare species using three types of surrogates (indicator species, vegetation composition and structure, and topoclimate) as predictors. We evaluated each type of surrogate alone and in combination with others. Location Data for our analyses were collected from 1996–2004 in three adjacent mountain ranges in the central Great Basin (Lander and Nye counties, Nevada, USA), the Shoshone Mountains, Toiyabe Range and Toquima Range. Methods Data on species richness and species composition of butterflies and birds and measures of vegetation composition and structure were obtained in the field. Topoclimatic variables were derived by GIS from digital sources and satellite images. We used Poisson regression with Bayesian model averaging to predict species richness and the number of rare species. We compared the expected prediction success of all models on the basis of internal and external validation trials. Results Same‐taxon indicator species were the most accurate predictors of species richness and of the number of rare species of butterflies and birds. Cross‐taxon indicator species and topoclimate variables were reasonably accurate predictors of species richness of butterflies and birds and of the number of rare butterfly species. Although vegetation variables were more effective for predicting species richness and number of rare species of birds than of butterflies, they were the least accurate predictors overall. Main conclusions Although indicator species may provide the most accurate predictions of species richness, their practical value, like any surrogate measure, depends greatly on ecological considerations and land‐use context. In general, the ability to predict numbers of rare species based on any set of candidate predictors was weaker than the ability to predict species richness, which may result from the high degree of stochasticity that often characterizes distributions of rare species. Our statistical approach for objective examination of different candidate predictors can help ensure that selection of species‐richness surrogates in any system is scientifically reliable and cost‐effective.     

Latitudinal gradient in species richness of the New World Triatominae (Reduviidae)

Aim To quantify the latitudinal gradient in species richness in the New World Triatominae and to explore the species‐energy and area hypotheses as possible causes. Location The gradient was studied for North and South America, between 43° N and 32° S. Methods A database was constructed containing the geographical distribution of the 118 New World Triatominae species based on data extracted from several published sources. Species richness was recorded as the number of species present within 5° latitudinal bands. We used univariate and multivariate models to analyse the relationship between area within each latitudinal belt, land surface temperature, and potential evapotranspiration as explanatory variables, and species richness. All variables were georeferenced and data were extracted using a GIS. Results Species richness of Triatominae increases significantly from the poles towards the Equator, peaking over the 5°?10 ° S latitudinal band. It increases according to a linear model, both north and south of the Equator, although a quadratic model fits better to southern hemisphere data. Richness correlates with habitable geographical area, when it is analysed through a nonlinear multiple regression factoring out latitude, only in the southern hemisphere. Regarding the species‐energy hypothesis, a multiple regression analysis controlling the effect of latitude shows a significant relationship between temperature and species richness. This effect is more pronounced in the southern hemisphere. Species richness shows a strong longitudinal trend south of the Equator (increasing to the east), but not north of the Equator. This differential pattern is reflected in significant interactions between longitude and both latitude and temperature in models of the species richness of the New World Triatominae. Main conclusions To our knowledge, this is the first time that a latitudinal gradient in species richness has been shown and analysed for obligate haematophagous organisms, and it shows that the species–energy hypothesis can account for this phenomenon. This relationship is stronger in the southern hemisphere.     

Habitat modeling used to predict relative abundance of bobcats in Iowa

We combined observations of bobcats (Lynx rufus) from bowhunters with remotely-sensed data to build models that describe habitat and relative abundance of this species in the agricultural landscape of Iowa, USA. We calculated landscape composition and configuration from publicly available land cover, census, road, hydrologic, and elevation data. We used multiple regression models to examine county-level associations between several explanatory variables and relative abundance of bobcats reported by surveyed bowhunters in each county. The most influential explanatory variables in the models were metrics associated with the presence of grassland, including Conservation Reserve, along with configuration of this perennial habitat with forests, although human population density and abundance of eastern cottontails (Sylvilagus floridanus) also correlated with abundance of bobcats. Validation of predictions against 3 years of independent data provided confidence in the models, with 66% of predictions within 1 bobcat/1,000 hunter-hours and 95% within 5 bobcats/1,000 hunter-hours of observed values. Once we accounted for landscape differences, no residual spatial trend was evident, despite relatively recent bobcat recolonization of Iowa. Models suggested that future range expansion of the bobcat population may be possible in some northern Iowa counties where habitat composition is similar to counties in southern Iowa where bobcats are abundant. Results from the county-level model have been useful to the Iowa Department of Natural Resources in evaluating the expansion of this once rare species and for delineating harvest opportunities. © 2011 The Wildlife Society.     

Global patterns and predictors of tropical reef fish species richness

In the marine realm, the tropics host an extraordinary diversity of taxa but the drivers underlying the global distribution of marine organisms are still under scrutiny and we still lack an accurate global predictive model. Using a spatial database for 6336 tropical reef fishes, we attempted to predict species richness according to geometric, biogeographical and environmental explanatory variables. In particular, we aimed to evaluate and disentangle the predictive performances of temperature, habitat area, connectivity, mid‐domain effect and biogeographical region on reef fish species richness. We used boosted regression trees, a flexible machine‐learning technique, to build our predictive model and structural equation modeling to test for potential ‘mediation effects’ among predictors. Our model proved to be accurate, explaining 80% of the total deviance in fish richness using a cross‐validated procedure. Coral reef area and biogeographical region were the primary predictors of reef fish species richness, followed by coast length, connectivity, mid‐domain effect and sea surface temperature, with interactions between the region and other predictors. Important indirect effects of water temperature on reef fish richness, mediated by coral reef area, were also identified. The relationship between environmental predictors and species richness varied markedly among biogeographical regions. Our analysis revealed that a few easily accessible variables can accurately predict reef fish species richness. They also highlight concerns regarding ongoing environmental declines, with region‐specific responses to variation in environmental conditions predicting a variable response to anthropogenic impacts.     

Plant species richness in continental southern Siberia: effects of pH and climate in the context of the species pool hypothesis

Aim Many high‐latitude floras contain more calcicole than calcifuge vascular plant species. The species pool hypothesis explains this pattern through an historical abundance of high‐pH soils in the Pleistocene and an associated opportunity for the evolutionary accumulation of calcicoles. To obtain insights into the history of calcicole/calcifuge patterns, we studied species richness–pH–climate relationships across a climatic gradient, which included cool and dry landscapes resembling the Pleistocene environments of northern Eurasia. Location Western Sayan Mountains, southern Siberia. Methods Vegetation and environmental variables were sampled at steppe, forest and tundra sites varying in climate and soil pH, which ranged from 3.7 to 8.6. Species richness was related to pH and other variables using linear models and regression trees. Results Species richness is higher in areas with warmer winters and at medium altitudes that are warmer than the mountains and wetter than the lowlands. In treeless vegetation, the species richness–pH relationship is unimodal. In tundra vegetation, which occurs on low‐pH soils, richness increases with pH, but it decreases in steppes, which have high‐pH soils. In forests, where soils are more acidic than in the open landscape, the species richness–pH relationship is monotonic positive. Most species occur on soils with a pH of 6–7. Main conclusions Soil pH in continental southern Siberia is strongly negatively correlated with precipitation, and species richness is determined by the opposite effects of these two variables. Species richness increases with pH until the soil is very dry. In dry soils, pH is high but species richness decreases due to drought stress. Thus, the species richness–pH relationship is unimodal in treeless vegetation. Trees do not grow on the driest soils, which results in a positive species richness–pH relationship in forests. If modern species richness resulted mainly from the species pool effects, it would suggest that historically common habitats had moderate precipitation and slightly acidic to neutral soils.     

Influence of Soil Physical Properties on Plants of the Mussununga Ecosystem, Brazil

Distribution ranges of plant species are related to physical variables of ecosystems that limit plant growth. Therefore, each plant species response to physical factors builds up the functional diversity of an ecosystem. The higher the species richness of an ecosystem, the larger the probability of maintaining functions and the higher the potential number of plant functional groups (FGs). Thus, the richness potentially increases the number of functions of the highly diverse Atlantic Rainforest domain in Brazil. Severe plant growth limitations caused by stress, however, decrease species richness. In the Spodosols of the Mussununga, an associated ecosystem of Atlantic Rainforest, the percentage of fine sand is directly related to water retention. Moreover, the depth of the cementation layer in the Mussununga??s sandy soil is a physical factor that can affect the plants?? stress gradients. When a shallow cementation layer depth is combined with low water retention in soils and with low fine sand percentage, the double stresses of flooding in the rainy season and water scarcity in the dry season result. This study aimed to identify FGs among Mussununga plant species responding to water stress gradients of soil and to verify the effects of the gradients on plant species richness of the Mussununga. A canonical correspondence analysis (CCA) of species abundance and soil texture variables was performed on 18 plots in six physiognomies of the Mussununga. Species richness rarefactions were calculated for each vegetation form to compare diversity. The two main axes of the CCA showed two FGs responding to soil texture and cementation layer depth: stress tolerator species and mesic species. Physical variables affect plant diversity, with species richness rising as the fine sand proportion also rises in the Mussununga. The effect of the cementation layer is not significantly related to species richness variation.     

贵州省国家重点保护野生植物物种丰富度分布特征及保护优先区分析

该研究以国家重点保护野生植物为指示物种,结合贵州各县(市、区)的国家重点保护野生植物调查统计信息,基于GIS技术,分析了贵州省国家重点保护野生植物物种丰富度分布特征;应用Dobson算法筛选识别贵州国家重点保护野生植物的保护优先区,并利用全省已建自然保护区信息评价分析所筛选的保护优先区的保护现状。结果表明:(1)在贵州国家重点保护野生植物的物种丰富度分布上,总体来说全省南部地区高于北部地区,环省界区域往内陆延伸的物种丰富度明显呈现减少趋势。(2)在75%和100%的国家重点保护野生植物物种保护水平上,筛选识别出了荔波县等4个和17个县域为保护优先区,其中分别有1个和9个国家重点保护野生植物保护优先区内涵盖的自然保护区面积低于5%,且涉及的自然保护区中68.26%的面积是市/县级。(3)贵州国家重点保护野生植物物种较丰富的区域和全省生物多样性热点区域与所筛选识别的保护优先区有较好的空间对应关系。(4)贵州国家重点保护野生植物的就地保护应以国家重点保护野生植物地理分布丰富的区域结合本研究筛选的保护优先区为重点,进行优先保护。     

Energy determines broad pattern of plant distribution in Western Himalaya

Several factors describe the broad pattern of diversity in plant species distribution. We explore these determinants of species richness in Western Himalayas using high‐resolution species data available for the area to energy, water, physiography and anthropogenic disturbance. The floral data involves 1279 species from 1178 spatial locations and 738 sample plots of a national database. We evaluated their correlation with 8‐environmental variables, selected on the basis of correlation coefficients and principal component loadings, using both linear (structural equation model) and nonlinear (generalised additive model) techniques. There were 645 genera and 176 families including 815 herbs, 213 shrubs, 190 trees, and 61 lianas. The nonlinear model explained the maximum deviance of 67.4% and showed the dominant contribution of climate on species richness with a 59% share. Energy variables (potential evapotranspiration and temperature seasonality) explained the deviance better than did water variables (aridity index and precipitation of the driest quarter). Temperature seasonality had the maximum impact on the species richness. The structural equation model confirmed the results of the nonlinear model but less efficiently. The mutual influences of the climatic variables were found to affect the predictions of the model significantly. To our knowledge, the 67.4% deviance found in the species richness pattern is one of the highest values reported in mountain studies. Broadly, climate described by water–energy dynamics provides the best explanation for the species richness pattern. Both modeling approaches supported the same conclusion that energy is the best predictor of species richness. The dry and cold conditions of the region account for the dominant contribution of energy on species richness.     

Influence of settlement time, human population, park shape and age, visitation and roads on the number of alien plant species in protected areas in the USA

Abstract. I examined a data set of 77 protected areas in the USA (including national and state parks) to determine which of the following variables most strongly influence alien plant species richness: park area, climate (temperature and precipitation), native species richness, visitation rate, local human population size, total road length, park shape and duration of European settlement. Many of these predictor variables are intercorrelated, so I used multiple regression to help separate their effects. In support of previous studies, native species richness was the best single predictor of alien species richness, probably because it was a good estimator of both park area and habitat diversity available for establishment of alien species. Other significant predictors of alien species richness were years of occupation of the area by European settlers and the human population size of adjacent counties. Climate, visitation rate, road length and park shape did not influence alien species richness. The proportion of alien species (alien richness/native richness) is inversely related to park area, in agreement with a previous study. By identifying which variables are most important in determining alien species richness, such findings suggest ways to reduce alien species establishment.     

Evaluating wetland losses with hydric soils

A study was conducted to determine if surveys of hydric soils could be used as a historic baseline to estimate wetland losses. Soils were digitized from county soil surveys and wetlands were digitized from National Wetlands Inventory (NWI) maps for two adjacent coastal counties in North Carolina. The two counties were located on the lower Atlantic Coastal Plain and have extensive areas of hydric soils, as much as 96% of the land surface area. Using hydric soils from soil surveys and wetlands from NWI maps, wetland losses since settlement were calculated to be 65% for Washington County and 38% for Tyrrell County. The NWI wetlands were compared to a mid-1950s wetlands survey to determine recent wetland losses. A large percentage of the wetland losses occurred between 1950 and 1980 for Washington County compard to Tyrrell County. Wetland losses for both counties occurred primarily on mineral hydric soils and the current wetlands distribution corresponded well with the distribution of organic soils.     

Forecasting insect species richness scores in poorly surveyed territories: the case of the Portuguese dung beetles (Col. Scarabaeinae)

Large-scale biodiversity assessment of faunal distribution is needed in poorly sampled areas. In this paper, Scarabaeinae dung beetle species richness in Portugal is forecasted from a model built with a data set from areas identified as well sampled. Generalized linear models are used to relate the number of Scarabaeinae species in each Portuguese UTM 50 × 50 grid square with a set of 25 predictor variables (geographic, topographic, climatic and land cover) extracted from a geographic information system (GIS). Between-squares sampling effort unevenness, spatial autocorrelation of environmental data, non-linear relationships between variables and an assessment of the models' predictive power, the main shortcomings in geographic species richness modelling, are addressed. This methodological approach has proved to be reliable and accurate enough in estimating species richness distribution, thus providing a tool to identify areas as potential targets for conservation policies in poorly inventoried countries.     

The ability of Nature Reserves to conserve medicinal plant resources: A case study in northeast China

Medicinal plants are important resources and are under serious threat due to human interference and climate change. We used species richness maps to find hotspots of medicinal plant localities and then modeled the environmental variables with a large effect on their distribution. We began by using a combination of species distribution models (SDMs) and geographic information system (GIS) tools to generate species richness maps of medicinal plants in northeast China. First, we conducted a detailed investigation of 2884 study plots in northeast China and selected 49 medicinal plant species for further analysis. The field surveys performed for this study spanned four years and identified a large number of new populations of medicinal plants in the forests of northeast China. We modeled and mapped the potential distributions of these 49 species and found that species richness hotspots are concentrated in the eastern and northeastern areas of the study region. We then analyzed the results of jackknife tests and found that the most important environmental variables on medicinal plant distribution are related to precipitation. Finally, we used the geographic distribution of medicinal plant richness to evaluate the ability of existing Nature Reserves to conserve these plants. By acquiring model data and using SDM and GIS to evaluate the current distribution and richness of medicinal plants, we are able to evaluate their current protection status and make recommendations about their utilization. This analysis could be expanded to assess medicinal plant populations in other regions where there are adequate records of the current distribution of medicinal plants.     

Invasibility and species richness of island endemic arthropods: a general model of endemic vs. exotic species

Aim This paper has two objectives. First, we examine how a variety of biotic, abiotic and anthropogenic factors influence the endemic and introduced arthropod richness on an oceanic island. Second, we look at the relationship between the endemic and introduced arthropod richness, to ask whether areas with high levels of endemic species richness deter invasions. Location The work was carried out on a young volcanic island, Terceira, in the Azores. Methods We used standard techniques to collect data on arthropod species richness. Environmental data were obtained from the CIELO climatic model and using GIS. The explanatory value of environmental variables on a small‐scale gradient of endemic and exotic arthropod species richness was examined with generalized linear models (GLMs). In addition, the impact of both endemic and exotic species richness in the communities was assessed by entering them after the environmental variable(s) to see if they contributed significantly to the final model (the hierarchical method). Results Abiotic (climatic and geomorphological) variables gave a better explanation of the variation in endemic species richness, whereas anthropogenic variables explained most of the variation in introduced species richness. Furthermore, after accounting for all environmental variables, part of the unexplained variance in the endemic species richness is explained by the introduced species richness and vice‐versa. That is, areas with high levels of endemic species richness had many introduced species. There is evidence of a somewhat inverse spatial distribution between a group of oceanic‐type, forest‐dwelling, endemic, relict arthropods and a group of more generalist endemic arthropods that are able to survive in disturbed marginal sites particularly rich in non‐indigenous species. Main conclusions Richness of endemic species is mainly driven by abiotic factors such as a climatic axis (oceanic‐type localities with lower temperatures and summer precipitations) and a binary variable CALD (location of sites in caldeiras or ravines), whereas richness of introduced species depends on disturbance related factors. However, after factoring out these major influences, there is a correlation between endemic and introduced richness, suggesting that – independent of the environmental and geographical factors that affect the distribution of endemic or introduced species – the richest endemic assemblages are more prone to invasion, due probably to a facilitation process. Inconclusive evidence suggests that non‐indigenous species are limited to those sites under anthropogenic influence located mainly near forest edges, but the rate of expansion of those species to high‐altitude, core pristine sites has still to be tested.