Can we predict centres of plant species richness and rarity from environmental variables in sub‐Saharan Africa?

In order to investigate continental-scale patterns of plant species richness and rarity, distribution maps of 3661 plant species were digitized into a one degree grid of sub-Saharan Africa using the WORLDMAP computer programme. Cells with high species richness were also likely to be those containing the greatest number of species of restricted range, but areas such as the South African Cape and the Eastern Arc mountains were found to have more restricted-range species than predicted from their richness scores. The two environmental predictors which had the strongest individual relationships with both species richness and range-size rarity were absolute maximum annual temperature and mean monthly potential evapotranspiration. However, correlative predictive powers of these variables were low, with R =−0.58 and R =−0.54, respectively ( P  < 0.01). Multiple regression also failed to produce a strong explanatory model for observed continental-scale patterns of diversity. Spatial variability analysis showed that this was likely to be because different environmental parameters predicted different centres of richness and rarity. West African species richness was better predicted by absolute maximum annual temperature, whereas East African species richness was better predicted by mean monthly potential evapotranspiration.  © 2003 The Linnean Society of London, Botanical Journal of the Linnean Society , 2003, 142 , 187–197.     

Patterns of fish species richness in China's lakes

Aim To document the patterns of fish species richness and their possible causes in China's lakes at regional and national scales. Location Lakes across China. Methods We compiled data of fish species richness, limnological characteristics and climatic variables for 109 lakes across five regions of China: East region, Northeast region, Southwest region, North‐Northwest region, and the Tibetan Plateau. Correlation analyses, regression models and a general linear model were used to explore the patterns of fish species richness. Results At the national scale, lake altitude, energy availability (potential evapotranspiration, PET) and lake area explained 79.6% of the total variation of the lake fish species richness. The determinants of the fish richness pattern varied among physiographic regions. Lake area was the strongest predictor of fish species richness in the East and Southwest lakes, accounting for 22.2% and 82.9% of the variation, respectively. Annual PET explained 68.7% of the variation of fish richness in the Northeast lakes. Maximum depth, mineralization degree, and lake area explained 45.5% of the fish variation in the lakes of the North‐Northwest region. On the Tibetan Plateau, lake altitude was the first predictor variable, interpreting 32.2% of the variation. Main conclusions Lake altitude was the most important factor explaining the variation of fish species richness across China's lakes, and accounted for 74.5% of the variation. This may stem in part from the fact that the lakes investigated in our study span the largest altitudinal range anywhere in the world. The effects of the lake altitude on fish species richness can be separated into direct and indirect aspects due to its collinearity with PET. We also found that the fish diversity and its determinants were scale‐dependent. Fish species richness was probably energy‐determined in the cold region, while it was best predicted by the lake area in the relatively geologically old region. The independent variables we used only explained a small fraction of the variations in the lake fish species richness in East China and the Tibetan Plateau, which may be due to the effects of human activity and historical events, respectively.     

Modelling the species richness distribution of French dung beetles (Coleoptera, Scarabaeidae) and delimiting the predictive capacity of different groups of explanatory variables

Aim To predict French Scarabaeidae dung beetle species richness distribution, and to determine the possible underlying causal factors. Location The entire French territory has been studied by dividing it into 301 grid cells of 0.72 × 0.36 degrees. Method Species richness distribution was predicted using generalized linear models to relate the number of species with spatial, topographic and climate variables in grid squares previously identified as well sampled (n = 66). The predictive function includes the curvilinear relationship between variables, interaction terms and the significant third‐degree polynomial terms of latitude and longitude. The final model was validated by a jack‐knife procedure. The underlying causal factors were investigated by partial regression analysis, decomposing the variation in species richness among spatial, topographic and climate type variables. Results The final model accounts for 86.2% of total deviance, with a mean jack‐knife predictive error of 17.7%. The species richness map obtained highlights the Mediterranean as the region richest in species, and the less well‐explored south‐western region as also being species‐rich. The largest fraction of variability (38%) in the number of species is accounted for by the combined effect of the three groups of explanatory variables. The spatially structured climate component explains 21% of variation, while the pure climate and pure spatial components explain 14% and 11%, respectively. The effect of topography was negligible. Conclusions Delimiting the adequately inventoried areas and elaborating forecasting models using simple environmental variables can rapidly produce an estimate of the species richness distribution. Scarabaeidae species richness distribution seems to be mainly influenced by temperature. Minimum mean temperature is the most influential variable on a local scale, while maximum and mean temperature are the most important spatially structured variables. We suggest that species richness variation is mainly conditioned by the failure of many species to go beyond determined temperature range limits.     

Environmental correlates of species richness for native freshwater fish in Oregon, U.S.A.

Aim To better understand how environmental factors affect fish species richness across the state of Oregon. Location Oregon, U.S.A. Methods A database showing collection locations of 4911 fish specimens in the Oregon State University museum was modified by the Oregon Natural Heritage Program to include probable occurrences, and mapped within a grid of 375 hexagons that cover the state. The individual species maps of freshwater fish in Oregon were reviewed and revised by thirty regional fish biologists and then synthesized into a single map of native species richness. We used regression tree analysis (RTA) and multiple linear regression (MLR) to assess patterns of fish species richness with twenty environmental, three anthropogenic, and two historical variables. Results RTA explained 66% of the variation in native species richness, associating richness with annual air temperature range, minimum January temperature, introduced species richness, and stream density. MLR explained 68% of native species richness variation and associated richness with maximum July temperature, air temperature range, standard deviation of monthly temperature, stream density, introduced species richness, and basin connectivity. Main conclusions We conclude that for these data and at this scale, native fish species richness in Oregon is associated with annual climatic extremes, spatial variability of climate, stream density, basin connectivity, and introduced fishes.     

Cichlid species‐area relationships are shaped by adaptive radiations that scale with area

A positive relationship between species richness and island size is thought to emerge from an equilibrium between immigration and extinction rates, but the influence of species diversification on the form of this relationship is poorly understood. Here, we show that within‐lake adaptive radiation strongly modifies the species‐area relationship for African cichlid fishes. The total number of species derived from in situ speciation increases with lake size, resulting in faunas orders of magnitude higher in species richness than faunas assembled by immigration alone. Multivariate models provide evidence for added influence of lake depth on the species‐area relationship. Diversity of clades representing within‐lake radiations show responses to lake area, depth and energy consistent with limitation by these factors, suggesting that ecological factors influence the species richness of radiating clades within these ecosystems. Together, these processes produce lake fish faunas with highly variable composition, but with diversities that are well predicted by environmental variables.     

Patterns of woody plant species richness in the Iberian Peninsula: environmental range and spatial scale

Aim Climate‐based models often explain most of the variation in species richness along broad‐scale geographical gradients. We aim to: (1) test predictions of woody plant species richness on a regional spatial extent deduced from macro‐scale models based on water–energy dynamics; (2) test if the length of the climate gradients will determine whether the relationship with woody species richness is monotonic or unimodal; and (3) evaluate the explanatory power of a previously proposed ‘water–energy’ model and regional models at two grain sizes. Location The Iberian Peninsula. Methods We estimated woody plant species richness on grid maps with c. 2500 and 22,500 km² cell size, using geocoded data for the individual species. Generalized additive models were used to explore the relationships between richness and climatic, topographical and substrate variables. Ordinary least squares regression was used to compare regional and more general water–energy models in relation to grain size. Variation partitioning by partial regression was applied to find how much of the variation in richness was related to spatial variables, explanatory variables and the overlap between these two. Results Water–energy dynamics generate important underlying gradients that determine the woody species richness even over a short spatial extent. The relationships between richness and the energy variables were linear to curvilinear, whereas those with precipitation were nonlinear and non‐monotonic. Only a small fraction of the spatially structured variation in woody species richness cannot be accounted for by the fitted variables related to climate, substrate and topography. The regional models accounted for higher variation in species richness than the water–energy models, although the water–energy model including topography performed well at the larger grain size. Elevation range was the most important predictor at all scales, probably because it corrects for ‘climatic error’ due to the unrealistic assumption that mean climate values are evenly distributed in the large grid cells. Minimum monthly potential evapotranspiration was the best climatic predictor at the larger grain size, but actual evapotranspiration was best at the smaller grain size. Energy variables were more important than precipitation individually. Precipitation was not a significant variable at the larger grain size when examined on its own, but was highly significant when an interaction term between itself and substrate was included in the model. Main conclusions The significance of range in elevation is probably because it corresponds to several aspects that may influence species diversity, such as climatic variability within grid cells, enhanced surface area, and location for refugia. The relative explanatory power of energy and water variables was high, and was influenced by the length of the climate gradient, substrate and grain size of the analysis. Energy appeared to have more influence than precipitation, but water availability is also determined by energy, substrate and topographic relief.     

Regional coherence of climatic and lake thermal variables of four lake districts in the Upper Great Lakes Region of North America

1. Within a region with common climatic conditions, lake thermal variables should exhibit coherent variability patterns to the extent to which they are not influenced by lake specific features such as morphometry and water clarity. We tested the degree of temporal coherence in interannual variability for climatic variables (air temperature and solar radiation) among four lake districts in the Upper Great Lakes Region. We also tested the degree of coherence of lake thermal variables (near‐surface temperature, eplimnetic temperature, hypolimnetic temperature and thermocline depth) for lakes within these districts. 2. Our four lake districts included the Experimental Lakes Area in north‐western Ontario, the Dorset Research Centre area north of Toronto, Ontario, the Northern Highland Lake District in northern Wisconsin, and the Yahara Lakes near Madison in southern Wisconsin. Seventeen lakes were analyzed for lake thermal variables dependent on stratification. Another five lakes were added for the analysis of near‐surface temperature. 3. The analysis tested whether for monthly and summer means, the climate (air temperature and solar radiation) across the four lake districts was coherent interannually and whether variables which measure the thermal structure of the lakes were coherent interannually among lakes within each lake district and across the four lake districts. 4. Temporal coherence was estimated by the correlation between lake districts for meteorological variables and between lake pairs for lake thermal variables. Mean coherence and the percentage of correlations exceeding the 5% significance level were derived both within and between lake districts for lake thermal variables. 5. Across the four lake districts, summer mean air temperature was highly coherent while summer solar radiation was less coherent. Approximately 60–80% of the interannual variation in mean summer air temperature at a site occurred across the entire region. Less than 45% of the variation in solar radiation occurred across sites. 6. Epilimnetic temperature and the near‐surface temperature were highly coherent both within and between lake districts. The coherence of thermocline depth within and between lake districts was weaker. Hypolimnetic temperature was not coherent between lake districts for most lake pairs. It was coherent among lakes within some lake districts. 7. The influences of local weather and differences among lakes in water clarity are discussed in the context of differences in levels of coherence among lake thermal variables and among lake pairs for a given variable.     

Butterfly species richness in the north-west Mediterranean Basin: the role of natural and human-induced factors

Aim We developed a model enabling us to evaluate the contribution of both natural and human‐related factors to butterfly species richness in Catalonia, a Mediterranean area that harbours one of the most diverse butterfly faunas in Europe. Location The study was carried out in Catalonia (north‐east Iberian Peninsula), a region of 31,930 km² lying between the Pyrenees, the Ebro depression and the Mediterranean sea. Methods Data from the Catalan Butterfly Monitoring Scheme were used to assess butterfly species richness from 55 transects spread all over the region. Three groups of environmental variables likely to affect the presence of butterfly species were calculated, above all from geographic information system data: (1) climatology and topography, (2) vegetation structure and (3) human disturbance. Because climatic and topographic variables are expected to be strongly correlated, we first performed a principal component analysis (PCA) to create a summarizing factor that would account for most of the variance within this set of variables. Subsequently, a backward stepwise multiple regression was performed in order to assess the effects of environmental factors on butterfly species richness. Results A total of 131 species were detected in the monitoring transects, representing 75.7% of the butterfly fauna known from Catalonia. Mean species richness per transect and per year was 41.4, although values varied greatly among sites (range: 14–76.8). The final regression model explained more than 80% of the total variance, which indicated a strong association between butterfly species richness and the studied environmental factors. The model revealed the very important contribution of climatic and topographic variables, which were combined into a single factor in the PCA. In contrast to what has been found in other, more northerly countries, species richness was negatively correlated with temperature and positively correlated with rainfall, except for extreme cold and wet conditions. This may be a consequence of the predictably adverse effects of the Mediterranean summer drought on herbivorous insects, and the fact that the limits of distribution of many butterflies correlate well with climatic variables. Human disturbance (defined as the proportion of urban and agricultural landscape cover in buffer areas of 5 km around the transect sites) was the second most important predictor for species richness. We found that a significant decrease in species numbers was associated with an increase in human pressure, a finding that indicates that not only building development, but also modern‐day agricultural practices are detrimental to the conservation of Mediterranean butterflies. Surprisingly, vegetation variables had an almost negligible effect on butterfly species richness. Main conclusions Our findings strongly indicate that the current motors of global change will have a negative effect on Mediterranean butterfly assemblages. First, changes in land‐use are transforming and fragmenting the landscape into an inhospitable and less permeable matrix for butterflies. Secondly, the negative correlation between species richness and temperature will lead to a predictable loss of diversity over the coming years, as predicted in the most plausible scenarios of climate change. Considering the high butterfly richness characterizing the Mediterranean Basin, this future trend will pose a serious threat to biodiversity.     

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.     

Multi-species richness of boreal agricultural landscapes: effects of climate, biotope, soil and geographical location

Aim To assess the relative importance of climate, biotope and soil variables as well as geographical location for the species richness of plants, butterflies, day‐active macromoths and wild bees in boreal agricultural landscapes. Location A total of 68 agricultural landscapes located in southern Finland. Methods Generalized linear mixed models were used to analyse the effects of environmental (climate, biotope and soil) and spatial (latitude and longitude) variables on species richness of four taxa in 136 study squares of 0.25 km². Using partial regression, the variation in species richness was decomposed into the purely environmental fraction; the spatially structured environmental fraction; and the purely spatial fraction, including variables retained in cubic trend surface regression. Results Species richness of all taxa was positively correlated with temperature. Species richness of plants and butterflies was also positively correlated with the heterogeneity of landscape. The extent of low‐intensity agricultural land and forest had a positive effect, and the extent of cultivated field a negative effect on the species richness of these taxa. The effect of soil characteristics on the number of observed species was negligible for all taxa. The greatest part of the explained variation for all taxa was accounted for by the pure effect of geographical location. To a somewhat lesser extent, the species richness of plants, butterflies and bees was also related to the effects of spatially structured environmental variables, and the species richness of macromoths to the effects of environmental variables. Main conclusions Multi‐species richness of boreal agricultural landscapes at the scale of 0.25 km² was associated with the heterogeneity of the local landscape. However, large‐scale geographical variation in species richness was also observed, which indicates the importance of climate and geographical location for the taxa studied. These results highlight the fact that, even on a landscape scale, geographical factors should be taken into account in biodiversity studies. Heterogeneous agricultural landscapes, including forest and non‐crop biotopes, should be preserved or restored to maintain biodiversity.     

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.     

What drives elevational patterns of diversity? A test of geometric constraints,climate and species pool effects for pteridophytes on an elevational gradient in Costa Rica

Aim We studied pteridophyte species richness between 100 m and 3400 m along a Neotropical elevational gradient and tested competing hypotheses for patterns of species richness. Location Elevational transects were situated at Volcán Barva in the Braulio Carrillo National Park and La Selva Biological Station (100–2800 m) and Cerro de la Muerte (2700–3400 m), both on the Atlantic slope of Costa Rica, Central America. Method We analysed species richness on 156 plots of 20 × 20 m and measured temperature and humidity at four elevations (40, 650, 1800 and 2800 m). Species richness patterns were regressed against climatic variables (temperature, humidity, precipitation and actual evapotranspiration), regional species pool, area and predicted species number of a geometric null model (the mid‐domain effect, MDE). Results The species richness of the 484 recorded species showed a hump‐shaped pattern with elevation with a richness peak at mid‐elevations (c. 1700 m). The MDE was the single most powerful explanatory variable in linear regression models, but species richness was also associated strongly with climatic variables, especially humidity and temperature. Area and species pool were associated less strongly with observed richness patterns. Main conclusions Geometric models and climatic models exclusive of geometric constraints explained comparable amounts of the elevational variation in species richness. Discrimination between these two factor complexes is not possible based on model fits. While overall fits of geometric models were high, large‐ and small‐ranged species were explained by geometric models to different extents. Species with narrow elevational ranges clustered at both ends of the gradient to a greater extent than predicted by the MDE null models used here. While geometric models explained much of the pattern in species richness, we cannot rule out the role of climatic factors (or vice versa) because the predicted peak in richness from geometric models, the empirical peak in richness and the overlap in favourable environmental conditions all coincide at middle elevations. Mid‐elevations offer highest humidity and moderate temperatures, whereas at high elevations richness is reduced due to low temperatures, and at low elevations by reduced water availability due to high temperatures.     

Spatial nonstationarity and scale-dependency in the relationship between species richness and environmental determinants for the sub-Saharan endemic avifauna

Aim This article aims to test for and explore spatial nonstationarity in the relationship between avian species richness and a set of explanatory variables to further the understanding of species diversity variation. Location Sub‐Saharan Africa. Methods Geographically weighted regression was used to study the relationship between species richness of the endemic avifauna of sub‐Saharan Africa and a set of perceived environmental determinants, comprising the variables of temperature, precipitation and normalized difference vegetation index. Results The relationships between species richness and the explanatory variables were found to be significantly spatially variable and scale‐dependent. At local scales > 90% of the variation was explained, but this declined at coarser scales, with the greatest sensitivity to scale variation evident for narrow ranging species. The complex spatial pattern in regression model parameter estimates also gave rise to a spatial variation in scale effects. Main conclusions Relationships between environmental variables are generally assumed to be spatially stationary and conventional, global, regression techniques are therefore used in their modelling. This assumption was not satisfied in this study, with the relationships varying significantly in space. In such circumstances the average impression provided by a global model may not accurately represent conditions locally. Spatial nonstationarity in the relationship has important implications, especially for studies of species diversity patterns and their scaling.     

Rotifer species richness along an altitudinal gradient in the Alps

Aim Biodiversity patterns along altitudinal gradients are less studied in aquatic than terrestrial systems, even though aquatic sites provide a more homogeneous environment independent of moisture constraints. We studied the altitudinal species richness pattern for planktonic rotifers in freshwater lakes and identified the environmental predictors for which altitude is a proxy. Location Two hundred and eighteen lakes of Trentino–South Tyrol (Italy) in the eastern Alps; lakes covered 98% (range 65–2960 m above sea level) of the altitudinal gradient in the Alps. Methods We performed: (1) linear regression between species richness and altitude to evaluate the general pattern, (2) multiple linear regression between species richness and environmental predictors excluding altitude to identify the most important predictors, and (3) linear regression between the residuals of the best model of step (2) and altitude to investigate any additional explanatory power of altitude. Selection of environmental predictors was based on limnological importance and non‐parametric Spearman correlations. We applied ordinary least squares regression, generalized linear, and generalized least squares modelling to select the most statistically appropriate model. Results Rotifer species richness showed a monotonic decrease with altitude independent of scale effects. Species richness could be explained (R²= 51%) by lake area as a proxy for habitat diversity, reactive silica and total phosphorus as proxies for productivity, water temperature as a proxy for energy, nitrate as a proxy for human influence and north–south and east–west directions as covariates. These predictors completely accounted for the species richness–altitude pattern, and altitude had no additional effect on species richness. Main conclusions The linear decrease of species richness along the altitudinal gradient was related to the interplay of habitat diversity, productivity, heat content and human influence. These factors are the same in terrestrial and aquatic habitats, but the greater environmental stability of aquatic systems seems to favour a linear pattern.     

Species richness, rarity and endemicity on Italian offshore islands: complementary signals from island-focused and species-focused analyses

Aims To investigate the relative explanatory power of source faunas and geographical variables for butterfly incidence, frequency, richness, rarity, and endemicity on offshore islands. Location The western Italian offshore islands (Italy and Malta). Methods Thirty‐one islands were examined. Data were taken from our own field surveys and from the literature. Two approaches were undertaken, described as island‐focused and species‐focused, respectively. Offshore islands were allocated to their neighbouring source landmasses (Italian Peninsula, Sicily and Sardinia–Corsica) and compared with each other for faunal attributes, source and island geography. Generalized linear and stepwise multiple regression models were then used to determine the relationships of island species richness, rarity and endemicity with potential geographical predictors and source richness, rarity, and endemicity (island‐focused). Species frequency and incidence were assessed in relation to geographical and source predictors using stepwise linear and logistic regression, and inter‐island associations were examined using K‐Means clustering and non‐metric scaling (species‐focused). Results The analysis reveals firm evidence for the influence of the nearest large landmass sources on island species assemblages, richness, rarity and endemicity. A clear distinction in faunal affinities occurs between the Sardinian islands and islands lying offshore from the Italian mainland and Sicily. Islands neighbouring these three distinct sources differ significantly in richness, rarity and endemicity. Source richness, rarity, and endemicity have explanatory power for island richness, rarity, and endemicity, respectively, and together with island geography account for a substantial part of the variation in island faunas (richness 59%, rarity 60% and endemicity 64%). Source dominates the logistic regression parameters predicting the incidence of island species [13 (38%) of 34 species that could be analysed]; three ecological factors (source frequency, flight period and maximal altitude at which species live) explained 75% of the variation in the occurrence of species on the islands. Species found more frequently on islands occurred more frequently at sources, had longer flight periods, and occurred at lower altitudes at the sources. The incidence of most species on islands (84%) is correctly predicted by the same three variables. Main conclusions The Italian region of the Mediterranean Sea has a rich butterfly fauna comprising endemics and rare species as well as more cosmopolitan species. Analysis of island records benefited from the use of two distinct approaches, namely island‐focused and species‐focused, that sift distinct elements in island and source faunas. Clear contemporary signals appear in island–source relationships as well as historical signals. Differences among faunas relating to sources within the same region caution against assuming that contemporary (ecological) and historical (evolutionary) influences affect faunas of islands in different parts of the same region to the same extent. The implications of source–island relationships for the conservation of butterflies within the Italian region are considered, particularly for the long‐term persistence of species.     

Water–energy,land‐cover and heterogeneity drivers of the distribution of plant species richness in a mountain region of the European Alps

Aim To evaluate the relative importance of water–energy, land‐cover, environmental heterogeneity and spatial variables on the regional distribution of Red‐Listed and common vascular plant species richness. Location Trento Province (c. 6200 km²) on the southern border of the European Alps (Italy), subdivided regularly into 228 3′ × 5′ quadrants. Methods Data from a floristic inventory were separated into two subsets, representing Red‐Listed and common (i.e. all except Red‐Listed) plant species richness. Both subsets were separately related to water–energy, land‐cover and environmental heterogeneity variables. We simultaneously applied ordinary least squares regression with variation partitioning and hierarchical partitioning, attempting to identify the most important factors controlling species richness. We combined the analysis of environmental variables with a trend surface analysis and a spatial autocorrelation analysis. Results At the regional scale, plant species richness of both Red‐Listed and common species was primarily related to energy availability and land cover, whereas environmental heterogeneity had a lesser effect. The greatest number of species of both subsets was found in quadrants with the largest energy availability and the greatest degree of urbanization. These findings suggest that the elevation range within our study region imposes an energy‐driven control on the distribution of species richness, which resembles that of the broader latitude gradient. Overall, the two species subsets had similar trends concerning the relative importance of water–energy, land cover and environmental heterogeneity, showing a few differences regarding the selection of some predictors of secondary importance. The incorporation of spatial variables did not improve the explanatory power of the environmental models and the high original spatial autocorrelation in the response variables was reduced drastically by including the selected environmental variables. Main conclusions Water–energy and land cover showed significant pure effects in explaining plant species richness, indicating that climate and land cover should both be included as explanatory variables in modelling species richness in human‐affected landscapes. However, the high degree of shared variation between the two groups made the relative effects difficult to separate. The relatively low range of variation in the environmental heterogeneity variables within our sampling domain might have caused the low importance of this complex factor.     

A spatio‐temporal framework for efficient inventories of natural resources: A case study with submersed macrophytes

Questions: Does a reduced nutrient load in open water increase species richness and the importance of regional and local site characteristics for species abundance and spatial distribution? Can we build lake‐specific models of macrophyte abundance and distribution based on site characteristics in order to prepare a cost‐efficient framework for future surveys? Location: Lake Constance, 47°39′N, 9°18′E. Methods: Generalized additive models (GAMs) were used to predict the potential distributions of eight species and overall species richness. Submersed macrophyte distribution in 1993 was compared with corresponding data from 1978, when eutrophication was at its maximum. Results: Spatial predictions for eight species and overall species richness were relatively accurate and independent of water chemistry. Depth was confirmed as a main predictor of species distribution, while effective fetch distance was retained in many models. Mineralogical variables of sediment composition represent allogenic and autogenic sediment sources and their east‐west gradient in Lake Constance corresponded to east‐west gradients of species distribution and richness. GAMs appeared more efficient than generalized linear models (GLMs) for modelling species responses to environmental gradients. Conclusions: Reduced trophic status increases species richness and the importance of regional and local site characteristics for species abundance and distribution. Our models represent a spatio‐temporal framework for future lake monitoring purposes and allow the development of effective monitoring; this could be generalized for many ecosystem types and would be particularly efficient for large lakes such as Lake Constance.     

Macroinvertebrate stream communities along regional and physico-chemical gradients in Western Greenland

1. Macroinvertebrates were collected and physico‐chemical variables measured at 16 stream sites in Western Greenland during July 1999. Eight sites were located on Disko Island in an arctic oceanic climate and eight sites in the Kangerlussuaq area close to the icecap where the climate is arctic continental. The streams had different water sources (glacial, groundwater, snowmelt and lake water). 2. The streams showed pronounced differences in water temperature (2.2–17.3 °C), concentrations of suspended solids (0–2400 mg L^?1), and conductivity (10–109 μS cm^?1). Principal component analysis (PCA) analysis of the physico‐chemical variables separated the Disko Island sites into a distinct group, whereas the sites in the Kangerlussuaq area were more dispersed. 3. A total of 56 macroinvertebrate species were found, including 31 species of Chironomidae, the most abundant of which was Orthocladius thienemanni. Diamesa sp. was only the sixth most abundant chironomid taxon. Species composition varied between sites, and abundance varied from about 20 individuals m^?2 in a glacier fed stream to more than 16 000 m^?2 in a lake outlet. 4. The macroinvertebrate communities of the 16 streams were separated into five TWINSPAN groups reflecting water source, irrespective of region. Lake outlets and ground‐water‐fed streams had the highest species richness and abundance, temperature and bed stability, while glacier‐fed streams were characterized by low species richness, abundance, temperature, bed stability and high concentrations of suspended solids. Macroinvertebrate species richness was positively correlated with water temperature and negatively with bed stability. Conductivity was positively correlated with invertebrate abundance. 5. The results of this study suggest that the source of stream water can be used to predict invertebrate community composition in Greenlandic streams and thus the effects of changes in water balance and flow regime, and to identify sites of special conservation interest.     

Plant species richness and diversity along an altitudinal gradient in the Sierra Nevada, Mexico

This article presents an analysis of plant species richness and diversity and its association with climatic and soil variables along a 1300‐m elevation gradient on the Cerro Tláloc Mountain in the northern Sierra Nevada in Mexico. Two 1000‐m² tree sampling plots were created at each of 21 selected sampling sites, as well as two 250‐m² plots for shrubs and six 9‐m² plots for herbaceous plants. Species richness and diversity were estimated for each plant life form, and beta diversity between sites was estimated along the gradient. The relationship between species richness and diversity and environmental variables was modelled using simple linear correlation and regression trees. Species richness and diversity showed a unimodal pattern with a bias towards high values in the lower half of the elevation gradient under study. This response was consistent for all three life forms. Beta diversity increased steadily along the elevation gradient, being lower between contiguous sites at intermediate elevations and high – the species replacement rate was nearly 100%– between sites at the extremes of the gradient. Few species were adapted to the full spectrum of environmental variation along the elevation gradient studied. The regression tree suggests that differences in species richness are mainly influenced by elevation (temperature and humidity) and soil variables, namely A₂ permanent wilting point, organic matter and horizon field capacity and A₁ horizon Mg²⁺.