Contrasting response of native and alien plant species richness to environmental energy and human impact along alpine elevation gradients

Aim We tested whether the species–energy and species–human relationships vary between native and both naturalized and casual alien species richness when other environmental variables had been taken into account. Location Trento Province, a region (c. 6200 km²) on the southern border of the European Alps (Italy), subdivided into 156 contiguous (c. 37.5 km²) cells and ranging in elevation from 66 to 3769 m. Methods Data were separated into three subsets, representing richness of natives, naturalized aliens and casual aliens and separately related to temperature, human population and various environmental correlates of plant species diversity. We applied ordinary least squares and simultaneous autoregressive regressions to identify potential contrasting responses of the three plant status subsets and hierarchical partitioning to evaluate the relative importance of the predictor variables. Results Variation in alien plant species richness along the region was almost entirely explained by temperature and human population density. The relationships were positive but strongly curvilinear. Native species richness was less strongly related to either factor but was positively related to the presence of calcareous bedrock. Native species richness had a decelerating positive relationship with temperature (R²= 55%), whereas naturalized and casual aliens had a positive accelerating relationship explaining 86% and 62% of the variation in richness, respectively. Native species richness had a positive decelerating relationship with population density (R²= 42%), whilst both alien subsets had a positive accelerating relationship. Main conclusions Alien species richness was higher in areas with the most rich and diverse assemblages of native species. Areas at high altitudes are not especially prone to alien invasion due to energy constraints, low propagule pressure and disturbance, even considering a potential increased in temperature. Thus, if we consider future environmental change, we should expect a stronger response of aliens than natives in the currently warm, urbanized, low‐altitude areas than in cold, high‐altitude areas where human population density is low.     

Plant species and growth form richness along altitudinal gradients in the southwest Ethiopian highlands

Questions: Do growth forms and vascular plant richness follow similar patterns along an altitudinal gradient? What are the driving mechanisms that structure richness patterns at the landscape scale? Location: Southwest Ethiopian highlands. Methods: Floristic and environmental data were collected from 74 plots, each covering 400 m². The plots were distributed along altitudinal gradients. Boosted regression trees were used to derive the patterns of richness distribution along altitudinal gradients. Results: Total vascular plant richness did not show any strong response to altitude. Contrasting patterns of richness were observed for several growth forms. Woody, graminoid and climber species richness showed a unimodal structure. However, each of these morphological groups had a peak of richness at different altitudes: graminoid species attained maximum importance at a lower elevations, followed by climbers and finally woody species at higher elevations. Fern species richness increased monotonically towards higher altitudes, but herbaceous richness had a dented structure at mid‐altitudes. Soil sand fraction, silt, slope and organic matter were found to contribute a considerable amount of the predicted variance of richness for total vascular plants and growth forms. Main Conclusions: Hump‐shaped species richness patterns were observed for several growth forms. A mid‐altitudinal richness peak was the result of a combination of climate‐related water–energy dynamics, species–area relationships and local environmental factors, which have direct effects on plant physiological performance. However, altitude represents the composite gradient of several environmental variables that were interrelated. Thus, considering multiple gradients would provide a better picture of richness and the potential mechanisms responsible for the distribution of biodiversity in high‐mountain regions of the tropics.     

Neophyte species richness at the landscape scale under urban sprawl and climate warming

Aim Land use and climate are two major components of global environmental change but our understanding of their simultaneous and interactive effects upon biodiversity is still limited. Here, we investigated the relationship between the species richness of neophytes, i.e. non‐native vascular plants introduced after 1500 AD, and environmental covariates to draw implications for future dynamics under land‐use and climate change. Location Switzerland, Central Europe. Methods The distribution of vascular plants was derived from a systematic national grid of 1 km² quadrates (n = 456; Swiss Biodiversity Monitoring programme) including 1761 species, 122 of which were neophytes. Generalized linear models (GLMs) were used to correlate neophyte species richness with environmental covariates. The impact of land‐use and climate change was thereafter evaluated by projections for the years 2020 and 2050 using scenarios of moderate and strong changes for climate warming (IPCC) and urban sprawl (NRP 54). Results Mean annual temperature and the amount of urban areas explained neophyte species richness best, with a high predictive power of the corresponding model (cross‐validated D² = 0.816). Climate warming had a stronger impact on the potential increase in the mean neophyte species richness (up to 191% increase by 2050) than ongoing urban sprawl (up to 10% increase) independently from variable interactions and model extrapolations to non‐analogue environments. Main conclusions In contrast to other vascular plants, the prediction of neophyte species richness at the landscape scale in Switzerland requires few variables only, and regions of highest species richness of the two groups do not coincide. The neophyte species richness is basically driven by climatic (temperature) conditions, and urban areas additionally modulate small‐scale differences upon this coarse‐scale pattern. According to the projections climate warming will contribute to the future increase in neophyte species richness much more than ongoing urbanization, but the gain in new neophyte species will be highest in urban regions.     

Regional vs local drivers of phylogenetic and species diversity in stream fish communities

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What factors influence the diversity of saproxylic beetles? A multiscaled study from a spruce forest in southern Norway

The diversity in different groups of obligate saproxylic beetles was related to ecological variables at three levels of spatial scale in mature spruce-dominated forest. The variables were connected to: (i) decaying wood, (ii) wood-inhabiting fungi, (iii) the level of disturbance, (iv) landscape ecology, and (v) vegetational structure. Several strong relationships were found at medium (1 km²) and large scales (4 km²), while only weak relationships were found at a small scale (0.16 ha; 1 ha=10⁴ m²). This may be explained by the local variations in habitat parameters and the high mobilities of many beetle species. Factors connected to decaying wood and wood-inhabiting fungi were clearly the most important factors at all scale levels. In particular, the variables diversity of dead tree parts, number of dead trees of large diameter and number of polypore fungi species increased the species richness of many groups and increased the abundance of many species. Eight species were absent below a certain density of decaying wood per 1 or 4 km². Former extensive cutting was a negative factor at large scale, probably because of decreasing recolonization with increasing distance to the source habitats. Thinning reduced the diversity of species associated with birch. The development of guidelines favouring the diversity of saproxylic beetles are discussed below.     

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.     

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⁺.     

Species richness, area and climate correlates

Aim Species richness–area theory predicts that more species should be found if one samples a larger area. To avoid biases from comparing species richness in areas of very different sizes, area is often controlled by counting the numbers of co‐occupying species in near‐equal area grid cells. The assumption is that variation in grid cell size accrued from working in a three‐dimensional world is negligible. Here we provide a first test of this idea. We measure the surface area of c. 50 × 50 km and c. 220 × 220 km grid cells across western Europe. We then ask how variation in the area of grid cells affects: (1) the selection of climate variables entering a species richness model; and (2) the accuracy of models in predicting species richness in unsampled grid cells. Location Western Europe. Methods Models are developed for European plant, breeding bird, mammal and herptile species richness using seven climate variables. Generalized additive models are used to relate species richness, climate and area. Results We found that variation in the grid cell area was large (50 × 50 km: 8–3311 km²; 220 × 220: 193–55,100 km²), but this did not affect the selection of variables in the models. Similarly, the predictive accuracy was affected only marginally by exclusion of area within models developed at the c. 50 × 50 km grid cells, although predictive accuracy suffered greater reductions when area was not included as a covariate in models developed for c. 220 × 220 km grid cells. Main conclusions Our results support the assumption that variation in near‐equal area cells may be of second‐order importance for models explaining or predicting species richness in relation to climate, although there is a possibility that drops in accuracy might increase with grid cell size. The results are, however, contingent on this particular data set, grain and extent of the analyses, and more empirical work is required.     

Patterns of bird species richness and composition on islands off Arnhem Land,Northern Territory,Australia

The bird faunas of the adjacent Wessel and English Company island chains were sampled at two scales (0.25 ha quadrats and entire islands). Ninety‐six species were recorded from 226 quadrats, with the most frequently recorded species being mistletoebird Dicaeum hirundinaceum, brown honeyeater Lichmera indistincta, silver‐crowned friarbird Philemon argenticeps, bar‐shouldered dove Geopelia humeralis, northern fantail Rhipidura rufiventris and yellow white‐eye Zosterops lutea. At the quadrat scale, vegetation type was a major determinant of the abundance of individual species (and hence species composition), species richness and total bird abundance. Bird species composition and richness at the quadrat scale was also significantly affected by island isolation (particularly the amount of land within 20 km of the island perimeter). Island size had no effect on quadrat‐scale richness or total abundance. However, the abundance of 10 of the 38 most frequently recorded individual species was significantly related to island size, in most cases even when the comparison was restricted to similar habitats. The most striking cases were rufous fantail Rhipidura rufifrons, mangrove golden whistler Pachycephala melanura, brown honeyeater and yellow white‐eye, which were all significantly more abundant on smaller islands. One hundred and seventy‐one species were recorded from the 62 islands sampled. There was a very tight relationship between island size and the number of terrestrial species (73% of deviance explained) and of all species (84% of deviance explained). This relationship was improved (marginally) when isolation was included in the model. Ordination of islands by their terrestrial bird species composition was related to island size and isolation, and suggested an erratic species composition on small islands.     

Species richness of woody plants in the landscapes of Central Spain: the role of management disturbances,environment and non‐stationarity

Questions: How important is management disturbance on gamma species richness of woody plants at intermediate landscape scales? How is species richness related to other climatic and biotic factors in the study area? How does the assumption of spatial stationarity affect assessment of relationships among species richness and explanatory variables (e.g. management, biotic and climatic factors) across extensive study areas? Location: Central Spain (regions of Castilla y León, Madrid and Castilla‐La Mancha). Scale: Extent: 150 000 km². Grain: 25 km² (5 × 5‐km cells). Methods: Information from 21 064 plots from the 3SNFI was used to evaluate richness of tree and shrub species at intermediate landscape scales. In addition to variables well known to explain biodiversity, e.g. environmental and biotic factors, effect of management treatments was evaluated by assessing clearcutting, selection cutting, stand improvement treatments and agrosilvopastoral systems (dehesas). Results from GWR techniques were compared with those from OLS regression. Results: Patterns of gamma species richness, although strongly affected by both environmental and biotic variables, were also significantly modified by management factors. Species richness increased with percentage of selection cutting stands and improvement treatments but decreased with percentage of clearcutting stands. Reduced species richness of woody plants was associated with agrosilvopastoral practices. Species richness for trees was closely related to basal area, annual precipitation and topographic complexity; species richness for shrubs was closely related to topographic complexity and agrosilvopastoral systems. Most relationships between species richness and environmental or biotic factors were non‐stationary. Relationships between species richness and management effects tended to be stationary, with a few exceptions. Conclusions: Landscape models of biodiversity in Central Spain were more informative when they accounted for effects of management practices, at least at intermediate scales. In the context of current rural abandonment, silvicultural disturbances of intermediate intensity increased gamma species richness of woody plants. Exclusion of factors such as agrosilvopastoral systems from models could have led to spurious relationships with other spatially co‐varying factors (e.g. summer precipitation). Patterns of spatial variation in relationships, provided by GWR models, allowed formulating hypotheses about potential ecological processes underlying them, beyond generalizations resulting from global (OLS) models.     

Exploring anthropogenic and natural processes shaping fern species richness along elevational gradients

Aim (1) To explore the impact of land use, climate and environmental heterogeneity on fern species richness along a complete elevational gradient, and (2) to evaluate the relative importance of the three groups of variables within different elevational intervals. Location A temperate mountain region (55,507 km²) of Italy on the southern border of the European Alps divided into a regular grid of 1476 cells (grain 35.7 km²). Methods We applied multiple regression (spatial and non‐spatial) to determine the relative influence of the three groups of variables on species richness, including variation partitioning at two scales. We considered the whole gradient (all 1476 cells) to explain the overall elevational pattern of species richness, and we grouped the cells into elevational intervals of 500 m in order to evaluate the explanatory power of the predictors within different zones along the gradient. Results Species richness showed a hump‐shaped pattern with elevation, forming a plateau between 800 and 1500 m. The lowest species richness was found in warm and relatively dry disturbed lowlands. Moving upwards, the greatest species richness was found in forest‐dominated mid‐elevations with high environmental heterogeneity. At high elevations dominated by open natural habitats, where temperature and precipitation were relatively low, species richness declined but less sharply than in the lowlands. Although it was impossible to separate the effects of the three groups of predictors along the whole gradient, the analysis of separate elevational intervals shed light on their relative importance. The decline of species richness within lowlands was mainly related to a combined effect of deforestation and low environmental heterogeneity. In the middle part of the gradient, habitat heterogeneity and topographic roughness were positively associated with species richness. The richness decline within high‐elevation areas was related mostly to climatic constraints. Main conclusions Human impact due to land‐use modifications strongly affects the elevational pattern of species richness. It is therefore increasingly important to adopt a multiple‐hypothesis approach, taking anthropogenic effects explicitly into account when describing ecological processes along elevational gradients.     

Coarse‐scale plant species richness in relation to environmental heterogeneity

Abstract. We test to what extent mean environmental conditions and environmental heterogeneity are related to species richness in a regular geographical grid system (UTM) of 10 km × 10 km in the NE Iberian Peninsula (i.e. Catalonia, ca. 31 900 km²). Species richness of each UTM quadrat was estimated by compiling a large database (more than a million records) from bibliographic references and atlases. Mean environmental conditions of each quadrat were derived from climatic maps. Environmental heterogeneity was estimated from the diversity of geological substrates and climatic classes in each quadrat. The increase in effective (real) area due to topographic complexity was also considered (derived from the digital elevation model). The statistical analysis was performed by a weighted analysis of deviance assuming a negative binomial error distribution. The results suggest that species richness in the study area is a function of both within‐quadrat heterogeneity (specifically, effective area, heterogeneity of geological substrates, heterogeneity of January temperature) and mean environmental conditions (mean annual temperature, Thornthwaite moisture index and aspect). All these parameters showed a positive relationship with species richness. Quadrat heterogeneity accounted for ca. 2/3 of the explained deviance, suggesting the importance of environmental heterogeneity when using a geographical grid system. The study fits well with earlier results on the importance of climatic parameters on plant species richness and provides one of the few rigorous, quantitative, coarse‐scale studies testing environmental heterogeneity in plant species richness.     

Modelled photosynthesis predicts woody plant richness at three geographic scales across the north-western United States

Aim We analyse regional patterns of woody plant species richness collected from field data in relation to modelled gross photosynthesis, P_g, compare the performance of P_g in relation to other productivity surrogates, and examine the effect of increasing scale on the productivity–richness relationship. Location The forested areas in the north‐western states of Oregon, Washington, Idaho, and Montana, USA. Methods Data on shrub and tree species richness were assembled from federal vegetation surveys and compared with modelled growing season gross photosynthesis, P_g (the sum of above‐ and below‐ground production plus autotrophic respiration) and two measures of spatial heterogeneity. We analysed the productivity–richness relationship at different scales by changing the focus size through spatial aggregation of field plots using 100 and 1000 km² windows covering the study area. Regression residuals were plotted spatially. Using the best available tree data set (Continuous Vegetation Survey: CVS), we compared different productivity indices, such as actual evapotranspiration and average temperature, in their ability to predict patterns of tree species richness. Results The highest species richness (species/unit area) occurred at intermediate levels of productivity. After accounting for variable sampling intensity, the richness–productivity relationship improved as more field plots were aggregated. At coarser levels of aggregation, modelled productivity accounted for 57–71% of the variation in richness patterns for shrubs and trees (CVS data set). Measures of spatial heterogeneity accounted for more variation in richness patterns aggregated by 100 km² windows than aggregation by 1000 km² windows. P_g was a better predictor of tree richness in Oregon and Washington (CVS data set) than any surrogate productivity index. Main conclusions P_g was observed to be a strong unimodal predictor of both tree (CVS) and shrub (FIA) richness when field data were aggregated. For the tree data set examined, seasonally integrated estimates of photosynthesis (P_g) predicted tree richness patterns better than climatic indices did.     

Breeding bird diversity in relation to environmental gradients in China

Geographic variation in species richness has been explained by different theories such as energy, productivity, energy–water balance, habitat heterogeneity, and freezing tolerance. This study determines which of these theories best account for gradients of breeding bird richness in China. In addition, we develop a best-fit model to account for the relationship between breeding bird richness and environment in China. Breeding bird species richness in 207 localities (3271 km² per locality on average) from across China was related to thirteen environmental variables after accounting for sampling area. The Akaike's information criterion (AIC) was used to evaluate model performance. We used Moran's I to determine the magnitude of spatial autocorrelation in model residuals, and used simultaneous autoregressive model to determine coefficients of determination and AIC of explanatory variables after accounting for residual spatial autocorrelation. Of all environmental variables examined, normalized difference vegetation index, a measure of plant productivity, is the best variable to explain the variance in breeding bird richness. We found that species richness of breeding birds at the scale examined is best predicted by a combination of plant productivity, elevation range, seasonal variation in potential evapotranspiration, and mean annual temperature. These variables explained 47.3% of the variance in breeding bird richness after accounting for sampling area; most of the explained variance in richness is attributable to the first two of the four variables.     

A consistent occupancy–climate relationship across birds and mammals of the Americas

At broad spatial scales, species richness is strongly related to climate. Yet, few ecological studies attempt to identify regularities in the individual species distributions that make up this pattern. Models used to describe species distributions typically model very complex responses to climate. Here, we test whether the variability in the distributions of birds and mammals of the Americas relates to mean annual temperature and precipitation in a simple, consistent way. Specifically, we test if simple mathematical models can predict, as a first approximation, the geographical variation in individual species’ probability of occupancy for 3277 non‐migratory bird and 1659 mammal species. We find a Gaussian model, where the probability of occupancy of a 10⁴ km² quadrat decreases symmetrically and gradually around a species ‘optimal’ temperature and precipitation, was generally the best model, explaining an average of 35% of the deviance in probability of occupancy. The inclusion of additional terms had very small and idiosyncratic effects across species. The Gaussian occupancy–climate relationship appears general among species and taxa and explains nearly as much deviance as complex models including many more parameters. Therefore, we propose that hypotheses aiming to explain the broad‐scale distribution of species or species richness must also predict generally Gaussian occupancy–climate relationships. Synthesis Science aims to identify regularities in a complex natural world. General patterns should be identified before one searches for potential mechanisms and contingencies. However, species geographic distributions are often modelled as complex (sometimes black box), species‐specific, functions of their environment. We asked whether a simple model could account for as much of the geographic variation in a species' probability of occupancy, and be widely applicable across thousands of species. As a first approximation, we found that a simple Gaussian occupancy‐climate relationship is very common in Nature, whether it be causal or not.     

Modelling breeding habitat preferences of Bonelli’s eagle (Hieraaetus fasciatus) in relation to topography, disturbance, climate and land use at different spatial scales

Predictive models on breeding habitat preferences of Bonelli’s eagle (Hieraaetus fasciatus; Aves: Accipitridae) have been performed at four different spatial scales in Castellón province, East of Iberian Peninsula. The scales considered were: (1) nest site scale (1×1 km² Universal Transverse Mercator (UTM) square containing the nest); (2) near nest environment (3×3 km² UTM square); (3) home range scale (5×5 km² UTM square); and (4) landscape level scale (9×9 km² UTM square containing the above mentioned ones). Topographic, disturbance, climatic and land use factors were measured on a geographic information system (GIS) at occupied and unoccupied UTM squares. Logistic regression was performed by means of a stepwise addition procedure. We tested whether inclusion of new subset of variables improved the models by increasing the area under the receiver operator characteristic plot. At nest site scale, only topographic factors were considered as the most parsimonious predictors. Probability of species occurrence increases with slope in craggy areas at lower altitudes. At the 3×3 km² scale, climate and disturbance variables were included. At home range and landscape level scales, models included climate, disturbance, topographic and land use factors. Higher temperatures in January, template ones in July, higher rainfall in June, lower altitudes and higher slope in the sample unit increase probability of occurrence of Bonelli’s eagle at broadest scales. The species seems to prefer disperse forests, scrubland and agricultural areas. From our results, we consider that there is a hierarchical framework on habitat selection procedure. We suggest that it is necessary to analyse what key factors are affecting Bonelli’s eagle nest-site selection at every study area to take steps to ensure appropriate conservation measures. The combination of regression modelling and GIS will become a powerful tool for biodiversity and conservation studies, taking into account that application depends on sampling design and the model assumptions of the statistical methods employed. Finally, predictive models obtained could be used for the efficient monitoring of this scarce species, to predict range expansions or identify suitable locations for reintroductions, and also to design protected areas and to help on wildlife management.     

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.     

Alien and native plant life‐forms respond differently to human and climate pressures

Aim To investigate whether differences in the elevational trend in native and alien species richness were dependent on climate or human pressures. Specifically we tested whether life‐form and/or alien/native status modifies the response of plant species richness to human population and temperature along: (1) a complete elevational gradient, and (2) within separate elevational bands that, by keeping temperature within a narrow range, elucidate the effects of human pressures more clearly. Location Two provinces (c. 7507 km²) on the southern border of the European Alps (Italy), subdivided into 240 contiguous sampling cells (c. 35.7 km²). Methods We used an extensive dataset on alien and native species richness across an elevation gradient (20–2900 m a.s.l.). Richness of natives and naturalized aliens were separately related to temperature, human population and Raunkiaer life‐form using general linear mixed models. Life‐form describes different plant strategies for survival during seasons with adverse cold/arid conditions. Results The relationship between species richness and temperature for natives was strongly dependent on life‐form, while aliens showed a consistent positive trend. Similar trends across alien and native life‐forms were found for the relationship between species richness and human population along the whole gradient and within separate elevational bands. Main conclusions The absence of life‐form‐dependent responses amongst aliens supports the hypothesis that the distribution of alien plant species richness was more related to propagule pressure and availability of novel niches created by human activities than to climatic filtering. While climate change will potentially contribute to relaxing species thermal constraints, the response of alien species to future warming will also be contingent on changes in anthropogenic pressures.     

Geographical gradients of species richness: a test of the water‐energy conjecture of Hawkins et al. (2003) using European data for five taxa

Aims We present an analysis of grid‐based species‐richness data for European plants, mammals, birds, amphibians and reptiles, designed to test the proposition of Hawkins et al. (2003a ) that the single best factor describing richness variation switches from the water regime to the energy regime in the mid‐latitudes and that the ‘breakpoint’ is related to the physiological character of the taxa. We go on to develop subregional models showing the extent to which regional model fits vary as a function of the extent of the study system, and compare the relative performance of ‘water’, ‘energy’ and ‘water–energy’ models of richness for southern, northern and pan‐European models. Location Western Europe. Methods We use atlas data comprising species range data for 187 species of mammals, 445 species of breeding birds, 58 amphibians, 91 reptiles and 2362 plant species, inserted into a c. 50 × 50 km grid cell system. We used 11 modelled climate variables, averaged for the period 1961–90. Statistical analyses were carried out using generalized additive models (GAMs), with splines simplified to a maximum of four degrees of freedom, and we tested for spatial autocorrelation using Moran's I values obtained at 10 different distance intervals. We selected favoured models on the grounds of deviance explained combined with a simple parsimony criterion, such that we selected either: (1) the best two‐variable energy, water or water–energy model, or (2) a four‐variable water–energy model, where the latter improved on the best two‐variable model by a minimum of 5% deviance explained. Results Threshold energy values, at which richness shows a transition from an increasing to a decreasing function of annual solar radiation, were identified for all taxa apart from reptiles. We found conditional support for the switch from dominance of water variables (southern models) to energy variables (northern models). Our favoured models switched between ‘water’ and ‘energy’ for mammals, and between ‘energy’ and ‘water–energy’ for birds, depending on whether we used data of pan‐European extent, southern or northern subsets. Deviance explained in our favoured models varied from 15% (birds, southern Europe) to 72% (amphibians, northern Europe), i.e. ranging from very poor to good fits with the data. Comparison with previous work indicates that our models are generally consistent with (if sometimes weaker than) previous findings. Main conclusions Our models are incomplete representations of factors influencing macro‐scale richness patterns across Europe, taking no explicit account of, for example, topographic variation, human influences or long‐term climatic variation. However, with the exception of birds, for which only the northern model attains over one‐third deviance explained, the models show that climate can account for meaningful proportions of the deviance. We find general support for considering water and energy regimes together in modelling species richness, and for the proposition that water is more limiting in southern Europe and energy in the north. Our analyses demonstrate the sensitivity of model outcomes to the geographical location and extent of the study system, illustrating that simple curve‐fitting exercises like these, particularly if based on regions with the complex history and geography characteristic of Europe, are unlikely to provide the basis for global, predictive models. However, such exercises may be of value in detecting which aspects of water and energy regimes may be of most importance in refining independently generated global models for regional application.