Spatial and environmental determinants of vascular plant species richness distribution in the Iberian Peninsula and Balearic Islands

Using an exhaustive data compilation, Iberian vascular plant species richness in 50 times 50 UTM grid cells was regressed against 24 explanatory variables (spatial, geographical, topographical, geological, climatic, land use and environmental diversity variables) using Generalized Linear Models and partial regression analysis in order to ascertain the relative contribution of primary, heterogeneous and spatially structured variables. The species richness variation accounted for by these variables is reasonably high (65% of total deviance). Little less than half of this variation is accounted for spatially structured variables. A purely spatial component of variation is hardly significant. The most significant variables are those related to altitude, and particularly maximum altitude, whose cubic response reflects the occurrence of the maximum number of species at the highest altitudes. This result highlighted the importance of Iberian mountains as hotspots of diversity and the relevance of large and small scale historical factors in contemporary plant distribution patterns. Climatic or energy-related variables contributed little, whereas geological (calcareous and acid rocks) and, to a lesser extent, environmental heterogeneity variables (land use diversity and altitude range) seem to be more important.     

Factors controlling the spatial species richness pattern of four groups of terrestrial vertebrates in an area between two different biogeographic regions in northern Spain

Aim To examine the influence of environmental variables on species richness patterns of amphibians, reptiles, mammals and birds and to assess the general usefulness of regional atlases of fauna. Location Navarra (10,421 km²) is located in the north of the Iberian Peninsula, in a territory shared by Mediterranean and Eurosiberian biogeographic regions. Important ecological patterns, climate, topography and land‐cover vary significantly from north to south. Methods Maps of vertebrate distribution and climatological and environmental data bases were used in a geographic information systems framework. Generalized additive models and partial regression analysis were used as statistical tools to differentiate (A) the purely spatial fraction, (B) the spatially structured environmental fraction and (C) the purely environmental fraction. In this way, we can evaluate the explanatory capacity of each variable, avoiding false correlations and assessing true causality. Final models were obtained through a stepwise procedure. Results Energy‐related features of climate, aridity and land‐cover variables show significant correlation with the species richness of reptiles, mammals and birds. Mammals and birds exhibit a spatial pattern correlated with variables such as aridity index and vegetation land‐cover. However, the high values of the spatially structured environmental fraction B and the low values of the purely environmental fraction A suggest that these predictor variables have a limited causal relationship with species richness for these vertebrate groups. An increment in land‐cover diversity is correlated with an increment of specific richness in reptiles, mammals and birds. No variables were found to be statistically correlated with amphibian species richness. Main conclusions Although aridity and land‐cover are the best predictor variables, their causal relationship with species richness must be considered with caution. Historical factors exhibiting a similar spatial pattern may be considered equally important in explaining the patterns of species richness. Also, land‐cover diversity appears as an important factor for maintaining biological diversity. Partial regression analysis has proved a useful technique in dealing with spatial autocorrelation. These results highlight the usefulness of coarsely sampled data and cartography at regional scales to predict and explain species richness patterns for mammals and birds. The accuracy of models appears to be related to the range perception of each group and the scale of the information.     

Freshwater biodiversity at regional extent: determinants of macroinvertebrate taxonomic richness in headwater streams

We studied spatial variation of macroinvertebrate species richness in headwater streams at two spatial extents, within and across drainage systems, and assessed the relative importance of three groups of variables (local, landscape and regional) at each extent. We specifically asked whether the same variables proposed to control broad‐scale richness patterns of terrestrial organisms (temperature, topographic variability) are important determinants of species richness also in streams, or whether environmental factors effective at mainly local scales (in‐stream heterogeneity, potential productivity) constrain species richness in local communities. We used forward selection with two stopping criteria to identify the key environmental and spatial variables at each study extent. Eigenvector‐based spatial filtering was applied to evaluate spatial patterns in species richness, and variation partitioning was used to assess the amount of variation in richness attributable to purely environmental and spatial components. A prime regulator of richness variation at the bioregion extent was elevation range (increasing richness with higher topographic variability), whereas hydrological stability and temperature were unimportant. Water chemistry variables, particularly water color, exhibited strong spatially‐structured variation across drainage systems. Local environmental variables explained most of the variation in species richness at the drainage‐system extent, reflecting gradients in total phosphorus and water color (negative effect on richness). The importance of the pure spatial component was strongly region‐dependent, with a peak (60%) in one drainage system, suggesting the presence of unmeasured environmental factors. Our results emphasize the need for spatially‐explicit, regional studies to better understand geographical variation of freshwater biodiversity. Future studies need to relate species richness not only to local factors but also to broad‐scale climatic variables, recognizing the presence of spatially‐structured environmental variation.     

Partitioning sources of variation in vertebrate species richness

Aim To explore biogeographic patterns of terrestrial vertebrates in Maine, USA using techniques that would describe local and spatial correlations with the environment. Location Maine, USA. Methods We delineated the ranges within Maine (86,156 km²) of 275 species using literature and expert review. Ranges were combined into species richness maps, and compared to geomorphology, climate, and woody plant distributions. Methods were adapted that compared richness of all vertebrate classes to each environmental correlate, rather than assessing a single explanatory theory. We partitioned variation in species richness into components using tree and multiple linear regression. Methods were used that allowed for useful comparisons between tree and linear regression results. For both methods we partitioned variation into broad‐scale (spatially autocorrelated) and fine‐scale (spatially uncorrelated) explained and unexplained components. By partitioning variance, and using both tree and linear regression in analyses, we explored the degree of variation in species richness for each vertebrate group that could be explained by the relative contribution of each environmental variable. Results In tree regression, climate variation explained richness better (92% of mean deviance explained for all species) than woody plant variation (87%) and geomorphology (86%). Reptiles were highly correlated with environmental variation (93%), followed by mammals, amphibians, and birds (each with 84–82% deviance explained). In multiple linear regression, climate was most closely associated with total vertebrate richness (78%), followed by woody plants (67%) and geomorphology (56%). Again, reptiles were closely correlated with the environment (95%), followed by mammals (73%), amphibians (63%) and birds (57%). Main conclusions Comparing variation explained using tree and multiple linear regression quantified the importance of nonlinear relationships and local interactions between species richness and environmental variation, identifying the importance of linear relationships between reptiles and the environment, and nonlinear relationships between birds and woody plants, for example. Conservation planners should capture climatic variation in broad‐scale designs; temperatures may shift during climate change, but the underlying correlations between the environment and species richness will presumably remain.     

鸟类迁徙与滨海旅游项目关系——以碧海银沙为例

2018年7月—2019年5月,采用样线法和样点法对启东长江口(北支)自然保护区恒大海上威尼斯碧海银沙海水浴场内鸟类多样性进行了调查,共记录到鸟类9目23科51种,其中鸻形目、雁形目、鹳形目、鸊鷉目等水鸟为鸟类群落的构成主体.不同季节鸟类多样性指数(H)依次为秋季迁徙期＞春季迁徙期＞夏季繁殖期＞越冬期,鸟种由多至少依次...     

Macroecological explanations for differences in species richness gradients: a canonical analysis of South American birds

Aim To evaluate how spatial variation of species richness in different bird orders responds to environmental gradients and determine which order level trait best predicts these relationships. Location South America. Methods A canonical correlation analysis was performed between the species richness in each of 17 bird orders and eight environmental variables in 374, 220 × 220 km cells. Loadings associated with the first two canonical variables were regressed against six order‐level predictors, including diversification level (number of species in each order), body size, median geographical range size and characteristics included in the model to control Type I error rates (the phylogenetic relationship among orders and levels of local‐scale spatial autocorrelation). Results Richness patterns of 14 bird orders were highly correlated with the first canonical axis, indicating that most orders respond similarly to energy‐water gradients (primarily actual evapotranspiration, minimum temperature and potential evapotranspiration). In contrast, species richness within Trochiliformes, Apodiformes and Galliformes were also correlated with the second canonical variable, representing measures of mesoscale climatic variation (range in elevation within cells, minimum temperature, and the interaction term between them) and landcover (habitat diversity). We also found that total diversification within orders was the best predictor of the loadings associated with the first canonical axis, whereas body size of each order best predicted loadings on the second axis. Conclusion Our results broadly support climatic‐related hypotheses as explanations for spatial variation in species richness of different orders. However, both historical (order‐specific variation in speciation rates) and ecological (dispersal of species that evolved by independent processes into areas amenable to birds) processes can explain the relationship between order level traits, such as body size and diversification level, and magnitude of response to current environment, furnishing then guidelines for a further and deeper understanding of broad‐scale diversity gradients.     

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.     

Spatial and environmental components of variation in the distribution patterns of subarctic plant species at Kevo, N Finland — a case study at the meso-scale level

This study presents a quantitative partitioning of the total variance in the patterns of occurrence of 231 vascular plant taxa in 362 1 × 1 km grids in the Kevo Nature Reserve into four independent components: purely spatial variation, spatially structured environmental variation, non-spatial environmental variation, and unexplained variation. This partitioning is done with (partial) constrained ordinations (canonical correspondence analysis) and associated Monte Carlo permutation tests. The numerical results suggest that most of the biological variance captured by the external explanatory variables is related to 'local' meso-scale environmental factors, as 12.6% of the variation in the species data is explained solely by the environmental variables. Part of the variance (6%) represents a spatially covarying environmental component, but only a very small part, ca 2%, is related to purely spatial variation. The amount of unexplained variation is very high (>75%). The results are compared and discussed in relation to the relative amounts of these four variance components at broader- and finer-scales and to the concepts of domains and transition zones of scales in biological patterning.     

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.     

Avifaunal gradients in two arid zones of central Iran in relation to vegetation, climate, and topography

Aim To identify the ecological factors (related to vegetation, altitude, climate, or geomorphology) that could explain the main gradients of avifaunal richness and composition in arid environments of Central Iran. Location The study was carried out in two nearby semi‐arid protected areas of the Ispahan province: the Kolah‐Ghazi National Park (c. 50,000 ha; 1540–2535 m a.s.l.) and the Mouteh Wildlife Refuge (c. 220,000 ha; 1493–2900 m a.s.l.). Annual average precipitation and temperature range from 155–205 mm and 15–19.5 °C, respectively. Vegetation cover is sparse. Methods The two study areas were sampled with a 1 × 1 km grid design. A subset of 405 squares was randomly chosen and each was visited once during spring or summer by a team of five observers walking from one side of a square to the other and back, recording the birds encountered. Raptors and species considered to be accidental or migrating were not taken into account. We first looked for avifaunal, vegetation, and geomorphological gradients using Correspondence Analysis. As we found spatial autocorrelation within our response variables (avifaunal richness, abundance and/or composition), we first calculated an autocorrelation term, then added this autocorrelation term in anova , ancova (separate slope design) and stepwise regression to assess the relative role of spatial autocorrelation and environmental explanatory variables (vegetation, altitude, climate, or topography). We also partitioned the variance of the avifaunal matrix between several sets of co‐variables, after controlling for spatial effects, using a series of partial Canonical Correspondence Analyses. Results A main gradient, common to the two areas, distinguishes bird species characteristic of flat sedimentary areas and species dwelling in mountainous and/or rocky areas. Despite the generally low values of the correlations measured, we found that richness, abundance and composition of the avifauna were better correlated with topography, especially the altitudinal amplitude within each square, than any other variable, including vegetation. Altitudinal amplitude is related to substrate complexity. Main conclusions In arid zones of central Iran, topographic features seem to be the main factors structuring avifaunal composition and abundance. Avifaunal composition and richness are mainly correlated with the complexity of the substrate, but both avifaunal richness and abundance increase with altitude, probably in response to decreasing aridity. We did not observe any bird altitudinal zonation in a strict sense. These results contrast with those generally observed in mid‐latitude regions of the Palaearctic.     

Importance of spatial autocorrelation in modeling bird distributions at a continental scale

Spatial autocorrelation in species' distributions has been recognized as inflating the probability of a type I error in hypotheses tests, causing biases in variable selection, and violating the assumption of independence of error terms in models such as correlation or regression. However, it remains unclear whether these problems occur at all spatial resolutions and extents, and under which conditions spatially explicit modeling techniques are superior. Our goal was to determine whether spatial models were superior at large extents and across many different species. In addition, we investigated the importance of purely spatial effects in distribution patterns relative to the variation that could be explained through environmental conditions. We studied distribution patterns of 108 bird species in the conterminous United States using ten years of data from the Breeding Bird Survey. We compared the performance of spatially explicit regression models with non-spatial regression models using Akaike's information criterion. In addition, we partitioned the variance in species distributions into an environmental, a pure spatial and a shared component. The spatially-explicit conditional autoregressive regression models strongly outperformed the ordinary least squares regression models. In addition, partialling out the spatial component underlying the species' distributions showed that an average of 17% of the explained variation could be attributed to purely spatial effects independent of the spatial autocorrelation induced by the underlying environmental variables. We concluded that location in the range and neighborhood play an important role in the distribution of species. Spatially explicit models are expected to yield better predictions especially for mobile species such as birds, even in coarse-grained models with a large extent.     

Flying with diverse passengers: greater richness of parasitic nematodes in migratory birds

Environmental changes are simultaneously affecting parasitic diseases and animal migrations, making it important to understand the disease dynamics of migratory species, including their range of infections and investment into defences. There is an urgent need for such knowledge because migratory animals, especially birds, are important for pathogen transmission and also particularly sensitive to environmental changes. Here we compare the nematode species richness and relative immune investment (via relative spleen size) of almost 200 migratory and non‐migratory species within three diverse groups of birds (Anseriformes, Accipitriformes and Turdidae) with worldwide distributions and varied ecology. Our results provide the first large‐scale demonstration that migratory birds face greater challenge from macroparasites as they have significantly dissimilar nematode fauna and higher nematode species richness compared to non‐migratory species. Even though birds with relatively large spleens had more nematode species, there was no difference in relative spleen size between migratory and non‐migratory bird species. The physiological stress of migration can be exacerbated by the potential range of pathologies induced by their richer nematode communities, particularly in combination with environmental perturbations. Altered migration stemming from global changes can also have important consequences for nematode transmission. Synthesis Most studies on parasites of migratory birds versus non‐migratory birds focus upon blood parasites; here we compared the diversity of another important parasite group – nematodes (roundworms) in three orders of birds. We found for any given order, migratory species and species with proportionally larger spleens generally have a wider range of nematodes. It is unclear why migratory species harbour more nematode species. Global climate change is expected to influence both bird migration patterns and infectious diseases, which may increase host susceptibility to parasitism and also introduce diverse nematodes to new areas and potential hosts.     

Hot,dry and different: Australian lizard richness is unlike that of mammals,amphibians and birds

Aims (1) To map the species richness of Australian lizards and describe patterns of range size and species turnover that underlie them. (2) To assess the congruence in the species richness of lizards and other vertebrate groups. (3) To search for commonalities in the drivers of species richness in Australian vertebrates. Location Australia. Methods We digitized lizard distribution data to generate gridded maps of species richness and β‐diversity. Using similar maps for amphibians, mammals and birds, we explored the relationship between species richness and temperature, actual evapotranspiration, elevation and local elevation range. We used spatial eigenvector filtering and geographically weighted regression to explore geographical patterns and take spatial autocorrelation into account. We explored congruence between the species richness of vertebrate groups whilst controlling for environmental effects. Results Lizard richness peaks in the central deserts (where β‐diversity is low) and tropical north‐east (where β‐diversity is high). The intervening lowlands have low species richness and β‐diversity. Generally, lizard richness is uncorrelated with that of other vertebrates but this low congruence is strongly spatially structured. Environmental models for all groups also show strong spatial heterogeneity. Lizard richness is predicted by different environmental factors from other vertebrates, being highest in dry and hot regions. Accounting for environmental drivers, lizard richness is weakly positively related to richness of other vertebrates, both at global and local scales. Main conclusions Lizard species richness differs from that of other vertebrates. This difference is probably caused by differential responses to environmental gradients and different centres of diversification; there is little evidence for inter‐taxon competition limiting lizard richness. Local variation in habitat diversity or evolutionary radiations may explain weak associations between taxa, after controlling for environmental variables. We strongly recommend that studies of variation in species richness examine and account for non‐stationarity.     

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.     

Determinants of species richness, endemism and turnover in European longhorn beetles

This study assessed the diversity patterns of a large family of beetles, Cerambycidae, in Europe and tested the following hypotheses: 1) richness gradients of this hyperdiverse taxon are driven by water and energy variables; 2) endemism is explained by the same factors, but variation between areas also reflects post‐glacial re‐colonization processes; and 3) faunal composition is determined by the same climatic variables and, therefore, beta diversity (species turnover) is related to richness gradients. Species richness, endemism and beta diversity were modelled using inventories of 37 European territories, built from a database containing the distributions of 609 species. Area, spatial position, and nine topographical and climatic variables were used as predictors in regression and constrained analysis of principal coordinates modelling. Species richness was mostly explained by a temperature gradient, which produced a south‐to‐north decreasing richness gradient. Endemism followed the same pattern, but was also determined by longitudinal variation, peaking in the southwestern and southeastern corners of the continent. Faunal turnover was explained by an important purely spatial pattern and a spatially structured environmental gradient. Thus, contrary to other groups, cerambycid richness was mostly explained by environmental energy, but not by water availability. Endemism was concentrated in the Iberian and Greek peninsulas, but not in Italy. Thus, the latter area may have been the major source of post‐glacial re‐colonization for European longhorn beetles or, otherwise, a poor refuge during glaciations. Turnover patterns were independent of the richness gradient, because northern faunas are nested in southern ones. Turnover, in contrast to richness, was driven by both the independent effects of climate and geographic constraints that might reflect dispersal limitation or stochastic colonization events, suggesting that richness gradients are more environmentally deterministic phenomena than turnover patterns.     

黄河流域鸟类多样性现状、分布格局及保护空缺

黄河流域幅员辽阔, 多样的地理气候、植被类型及人类活动塑造了多样化的生物多样性格局。本研究以IUCN与国际鸟盟发布的鸟类分布图层为基础, 同时收集了黄河流域2009-2019年的鸟类实地观测记录, 包括观鸟记录、GBIF数据库、红外相机监测及其他实地调查的鸟类数据, 共得到35,026条鸟类实地观测有效记录。汇总结果显示, 黄河流域记录有鸟类物种662种, 占中国鸟类物种总数的45.81%。这些鸟类分属于23目83科, 其中雀形目物种数最多(384种, 占本目全国鸟种总数的46.83%), 其次为鸻形目(67种, 占50.00%)和雁形目(39种, 占72.22%)。黄河流域受威胁鸟类共计121种, 其中有37种和52种分别在IUCN红色名录和《中国脊椎动物红色名录》中被列为受威胁物种(即评估级别为极危、濒危或易危), 22种和73种被分别列为国家I级和II级重点保护野生动物。这些受威胁鸟种多为地栖性、体型大、营养级高或具有长距离迁徙习性的物种。黄河流域鸟类整体物种多样性由南向北递减, 以黄河上中游四川、甘肃、陕西的高原与山地内鸟种最为丰富, 而受威胁鸟类物种多样性热点区则在黄河中下游, 下游黄河三角洲及邻近平原区为受威胁鸟类最主要集中分布区。黄河流域内48个国家级自然保护区共覆盖鸟种数504种(占黄河流域鸟类总种数的76.13%), 其中受威胁鸟种92种(占黄河流域受威胁鸟种数的76.03%)。区域内国家级自然保护区大多分布在黄河上游, 对黄河下游的受威胁物种覆盖程度较低, 保护空缺较严重。对此, 我们建议着重加强中下游自然保护区建设与能力提升, 增加对中下游受威胁鸟种的保护力度, 在保护策略上应当积极探索高强度土地利用下的多样化保护机制。     

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.     

Using patterns of distribution and diversity of Kenyan birds to select and prioritize areas for conservation

Patterns and environmental correlates of species distributions and richness are identified for Kenyan birds at a quarter degree-square scale. This information is used together with iterative complementarity analyses, which employ species richness, taxonomic dispersion and range-restrictedness, to identify priority areas for possible conservation attention. Bird species apparently not conserved by existing protected areas in Kenya are identified. Six avifaunal zones (and one transitional zone) are distinguished based on distributions of suites of bird species. Variation in biotope diversity (the number of forest and aquatic systems) accounts for 79% of the observed variation in Kenyan bird species richness. Although both rainfall and altitudinal range are significantly correlated with species richness, they only explain an additional 3% of the observed variation. The priority areas identified are situated mainly within highlands and coastal lowlands. Although few priority areas are identified in northern Kenya, this region also constitutes a priority, as it contains a suite of xeric species with habitats that are not represented elsewhere in Kenya. The papyrus yellow warbler, Chloropeta gracilirostris, William's bush lark, Mirafra williamsi, white-winged dove, Streptopelia reichenowi, and Jubaland weaver, Ploceus dichrocephalus, are identified as endemics or near-endemics that are probably not adequately conserved in Kenya at present.     

Multivariate analysis of a fine-scale breeding bird atlas using a geographical information system and partial canonical correspondence analysis: environmental and spatial effects

Aim To assess the relative roles of environment and space in driving bird species distribution and to identify relevant drivers of bird assemblage composition, in the case of a fine‐scale bird atlas data set. Location The study was carried out in southern Belgium using grid cells of 1 × 1 km, based on the distribution maps of the Oiseaux nicheurs de Famenne: Atlas de Lesse et Lomme which contains abundance for 103 bird species. Methods Species found in < 10% or > 90% of the atlas cells were omitted from the bird data set for the analysis. Each cell was characterized by 59 landscape metrics, quantifying its composition and spatial patterns, using a Geographical Information System. Partial canonical correspondence analysis was used to partition the variance of bird species matrix into independent components: (a) ‘pure’ environmental variation, (b) spatially‐structured environmental variation, (c) ‘pure’ spatial variation and (d) unexplained, non‐spatial variation. Results The variance partitioning method shows that the selected landscape metrics explain 27.5% of the variation, whilst ‘pure’ spatial and spatially‐structured environmental variables explain only a weak percentage of the variation in the bird species matrix (2.5% and 4%, respectively). Avian community composition is primarily related to the degree of urbanization and the amount and composition of forested and open areas. These variables explain more than half of the variation for three species and over one‐third of the variation for 12 species. Main conclusions The results seem to indicate that the majority of explained variation in species assemblages is attributable to local environmental factors. At such a fine spatial resolution, however, the method does not seem to be appropriated for detecting and extracting the spatial variation of assemblages. Consequently, the large amount of unexplained variation is probably because of missing spatial structures and ‘noise’ in species abundance data. Furthermore, it is possible that other relevant environmental factors, that were not taken into account in this study and which may operate at different spatial scales, can drive bird assemblage structure. As a large proportion of ecological variation can be shared by environment and space, the applied partitioning method was found to be useful when analysing multispecific atlas data, but it needs improvement to factor out all‐scale spatial components of this variation (the source of ‘false correlation’) and to bring out the ‘pure’ environmental variation for ecological interpretation.