Noncrop features and heterogeneity mediate overwintering bird diversity in agricultural landscapes of southwest China

Farmland birds are of conservation concerns around the world. In China, conservation management has focused primarily on natural habitats, whereas little attention has been given to agricultural landscapes. Although agricultural land use is intensive in China, environmental heterogeneity can be highly variable in some regions due to variations in crop and noncrop elements within a landscape. We examined how noncrop heterogeneity, crop heterogeneity, and noncrop features (noncrop vegetation and water body such as open water) influenced species richness and abundance of all birds as well as three functional groups (woodland species, agricultural land species, and agricultural wetland species) in the paddy‐dominated landscapes of Erhai water basin situated in northwest Yunnan, China. Birds, crop, and noncrop vegetation surveys in twenty 1 km × 1 km landscape plots were conducted during the winter season (from 2014 to 2015). The results revealed that bird community compositions were best explained by amounts of noncrop vegetation and compositional heterogeneity of noncrop habitat (Shannon–Wiener index). Both variables also had a positive effect on richness and abundance of woodland species. Richness of agricultural wetland species increased with increasing areas of water bodies within the landscape plot. Richness of total species was also greater in the landscapes characterized by larger areas of water bodies, high proportion of noncrop vegetation, high compositional heterogeneity of noncrop habitat, or small field patches (high crop configurational heterogeneity). Crop compositional heterogeneity did not show significant effects neither on the whole community (all birds) nor on any of the three functional groups considered. These findings suggest that total bird diversity and some functional groups, especially woodland species, would benefit from increases in the proportion of noncrop features such as woody vegetation and water bodies as well as compositional heterogeneity of noncrop features within landscape.     

宏生态尺度上景观破碎化对物种丰富度的影响

生物多样性的地理格局及其形成机制是宏生态学与生物地理学的研究热点。大量研究表明,景观尺度上的生境破碎化对物种多样性的分布格局具有重要作用,但目前尚不清楚这种作用是否足以在宏生态尺度上对生物多样性地理格局产生显著影响。利用中国大陆鸟类和哺乳动物的物种分布数据,在100 km×100 km网格的基础上生成了这两个类群生物的物种丰富度地理格局,进一步利用普通最小二乘法模型和空间自回归模型研究了物种丰富度与气候、生境异质性、景观破碎化的相关关系。结果表明,景观破碎化因子与鸟类和哺乳动物的物种丰富度都具有显著的关联关系,其方差贡献率可达约30%—50%(非空间模型)和60%—80%(空间模型),略低于或接近于气候和生境异质性因子。方差分解结果显示,景观破碎化因子与气候和生境异质性因子的方差贡献率的重叠部分达20%—40%。相对鸟类而言,景观破碎化对哺乳动物物种丰富度的地理格局具有更高的解释率。     

Determinants of species richness patterns in the Netherlands across multiple taxonomic groups

We examined the species richness patterns of five different species groups (mosses, reptiles and amphibians, grasshoppers and crickets, dragonflies, and hoverflies) in the Netherlands (41,500 km²) using sampling units of 5 × 5 km. We compared the spatial patterns of species richness of the five groups using Spearman’s rank correlation and used a stepwise multiple regression generalized linear modelling (GLM) approach to assess their relation with a set of 36 environmental variables, selected because they can be related to the several hypotheses on biodiversity patterns. Species richness patterns of the five groups were to a certain extent congruent. Our data suggest that environmental heterogeneity (in particular habitat heterogeneity) is one of the major determinants of variation in species richness within these five groups. We found that for taxonomic groups comprising a low number of species, our regression model explained more of the variability in species richness than for taxonomic groups with a large number of species.     

Species–area models to assess biodiversity change in multi-habitat landscapes: The importance of species habitat affinity

Species–area relationships (SARs) are a common tool to assess the impacts of habitat loss on species diversity. Species–area models that include habitat effects may better describe biodiversity patterns; also the shape of the SAR may be best described by other models than the classical power model. We compared the fit of 24 SAR models, i.e. eight basic models using three approaches: (i) single-habitat models, (ii) multi-habitat models which account for the effect of the habitat composition on total species diversity (= choros models) and (iii) multi-habitat models which also account for the differential use of habitats by different species groups (= countryside models). We use plant diversity data from a multi-habitat landscape in NW Portugal. Countryside models had the best fit both when predicting species–area patterns of species groups and of total species richness. Overall, choros models had a better fit than single-habitat models. We also tested the application of multi-habitat models to land-use change scenarios. Estimates of species richness using the choros model only depended on the number of habitats in the landscape. In contrast, for the countryside model, estimates of species richness varied continuously with the relative proportion of the different habitat types in the landscape, and projections suggest that land-use change impacts may be moderated by a species’ ability to use multiple habitats in the landscape. We argue that the countryside SAR is a better model to assess the impacts of land-use changes than the single-habitat SAR or the choros model, as species often face habitat change instead of real habitat loss, and species response to change is contingent on their differential use of habitats in the landscape.     

A habitat quality indicator for common birds in Europe based on species distribution models

The EU 2020 Biodiversity Strategy requires the gathering of information on biodiversity to aid in monitoring progress towards its main targets. Common species are good proxies for the diversity and integrity of ecosystems, since they are key elements of the biomass, structure, functioning of ecosystems, and therefore of the supply of ecosystem services. In this sense, we aimed to develop a spatially-explicit indicator of habitat quality (HQI) at European level based on the species included in the European Common Bird Index, also grouped into their major habitat types (farmland and forest). Using species occurrences from the European Breeding Birds Atlas (at 50 km × 50 km) and the maximum entropy algorithm, we derived species distribution maps using refined occurrence data based on species ecology. This allowed us to cope with the limitations arising from modelling common and widespread species, obtaining habitat suitability maps for each species at finer spatial resolution (10 km × 10 km grid), which provided higher model accuracy. Analysis of the spatial patterns of local and relative species richness (defined as the ratio between species richness in a given location and the average richness in the regional context) for the common birds analysed demonstrated that the development of a HQI based on species richness needs to account for the regional species pool in order to make objective comparisons between regions. In this way, we proved that relative species richness compensated for the bias caused by the inherent heterogeneous patterns of the species distributions that was yielding larger local species richness in areas where most of the target species have the core of their distribution range. The method presented in this study provides a robust and innovative indicator of habitat quality which can be used to make comparisons between regions at the European scale, and therefore potentially applied to measure progress towards the EU Biodiversity Strategy targets. Finally, since species distribution models are based on breeding birds, the HQI can be also interpreted as a measure of the capacity of ecosystems to provide and maintain nursery/reproductive habitats for terrestrial species, a key maintenance and regulation ecosystem service.     

Energy–water and seasonal variations in climate underlie the spatial distribution patterns of gymnosperm species richness in China

Studying the pattern of species richness is crucial in understanding the diversity and distribution of organisms in the earth. Climate and human influences are the major driving factors that directly influence the large‐scale distributions of plant species, including gymnosperms. Understanding how gymnosperms respond to climate, topography, and human‐induced changes is useful in predicting the impacts of global change. Here, we attempt to evaluate how climatic and human‐induced processes could affect the spatial richness patterns of gymnosperms in China. Initially, we divided a map of the country into grid cells of 50 × 50 km² spatial resolution and plotted the geographical coordinate distribution occurrence of 236 native gymnosperm taxa. The gymnosperm taxa were separated into three response variables: (a) all species, (b) endemic species, and (c) nonendemic species, based on their distribution. The species richness patterns of these response variables to four predictor sets were also evaluated: (a) energy–water, (b) climatic seasonality, (c) habitat heterogeneity, and (d) human influences. We performed generalized linear models (GLMs) and variation partitioning analyses to determine the effect of predictors on spatial richness patterns. The results showed that the distribution pattern of species richness was highest in the southwestern mountainous area and Taiwan in China. We found a significant relationship between the predictor variable set and species richness pattern. Further, our findings provide evidence that climatic seasonality is the most important factor in explaining distinct fractions of variations in the species richness patterns of all studied response variables. Moreover, it was found that energy–water was the best predictor set to determine the richness pattern of all species and endemic species, while habitat heterogeneity has a better influence on nonendemic species. Therefore, we conclude that with the current climate fluctuations as a result of climate change and increasing human activities, gymnosperms might face a high risk of extinction.     

Plant species richness–environment relationships across the Subantarctic–Patagonian transition zone

Aim To evaluate the relative importance of climate, productivity, environmental heterogeneity, biotic associations and habitat use by cattle to account for the species richness of trees, shrubs and herbs across the Subantarctic–Patagonian transition. Location An area of c. 150 × 150 km, within the transition zone between the Subantarctic and Patagonian subregions on the eastern slope of the Andes (c. 39–42° S, 70–72° W). Methods All vascular plants found at each one of 50 (10 × 10 m) sampling plots were counted to estimate the local tree, shrub and herb species richness. Path analysis was used to evaluate the relationship between the richness of the three life‐forms and plant cover, dried litter biomass, mean annual temperature, annual precipitation, daily temperature range, substrate heterogeneity and number of faecal pats. Principal coordinates of neighbour matrices was used to model the spatial autocorrelation of the data. Results Total plant species richness showed a unimodal pattern of spatial variation across the transition. Richness responded positively to indirect effects of precipitation mediated through plant cover, but there was a negative overall effect of precipitation on richness towards the west of the transition, most strongly for trees. An increase in substrate heterogeneity promoted a local increase in herb and shrub richness; the richness of trees increased in sites with steeper slopes. Canopy closure had a direct negative impact on herb richness; it also increased the local accumulation of litter, which negatively affected shrub and herb richness. The impact of habitat use by cattle negatively affected herb richness in areas to the east of the biogeographical transition. Main conclusions We suggest that the importance of indirect climatic effects mediated by vegetation cover can account for species richness patterns across this transition, most strongly for woody species, which supports the productivity hypothesis. The southern temperate forests towards the west may represent a deviation from the predictions of the water–energy dynamics hypothesis. Dissimilar spatial patterns of variation in the richness of woody and herbaceous species, and their different responses to climatic and heterogeneity variables across the transition, suggest that plant life‐form influences the plant species richness–environment relationships.     

How does forest landscape structure explain tree species richness in a Mediterranean context?

Although the strong relationship between vegetation and climatic factors is widely accepted, other landscape composition and configuration characteristics could be significantly related with vegetation diversity patterns at different scales. Variation partitioning was conducted in order to analyse to what degree forest landscape structure, compared to other spatial and environmental factors, explained forest tree species richness in 278 UTM 10 × 10 km cells in the Mediterranean region of Catalonia (NE Spain). Tree species richness variation was decomposed through linear regression into three groups of explanatory variables: forest landscape (composition and configuration), environmental (topography and climate) and spatial variables. Additionally, the forest landscape characteristics which significantly contributed to explain richness variation were identified through a multiple regression model. About 60% of tree species richness variation was explained by the whole set of variables, while their joint effects explained nearly 28%. Forest landscape variables were those with a greater pure explanatory power for tree species richness (about 15% of total variation), much larger than the pure effect of environmental or spatial variables (about 2% each). Forest canopy cover, forest area and land cover diversity were the most significant composition variables in the regression model. Landscape configuration metrics had a minor effect on forest tree species richness, with the exception of some shape complexity indices, as indicators of land use intensity and edge effects. Our results highlight the importance of considering the forest landscape structure in order to understand the distribution of vegetation diversity in strongly human-modified regions like the Mediterranean.     

Plant functional group composition and large‐scale species richness in European agricultural landscapes

Question: Which are the plant functional groups responding most clearly to agricultural disturbances? Which are the relative roles of habitat availability, landscape configuration and agricultural land use intensity in affecting the functional composition and diversity of vascular plants in agricultural landscapes? Location: 25 agricultural landscape areas in seven European countries. Methods: We examined the plant species richness and abundance in 4 km × 4 km landscape study sites. The plant functional group classification was derived from the BIOLFLOR database. Factorial decomposition of functional groups was applied. Results: Natural habitat availability and low land use intensity supported the abundance and richness of perennials, sedges, pteridophytes and high nature quality indicator species. The abundance of clonal species, C and S strategists was also correlated with habitat area. An increasing density of field edges explained a decrease in richness of high nature quality species and an increase in richness of annual graminoids. Intensive agriculture enhanced the richness of annuals and low nature quality species. Conclusions: Habitat patch availability and habitat quality are the main drivers of functional group composition and plant species richness in European agricultural landscapes. Linear elements do not compensate for the loss of habitats, as they mostly support disturbance tolerant generalist species. In order to conserve vascular plant species diversity in agricultural landscapes, the protection and enlargement of existing patches of (semi‐) natural habitats appears to be more effective than relying on the rescue effect of linear elements. This should be done in combination with appropriate agricultural management techniques to limit the effect of agrochemicals to the fields.     

Grids versus regional species lists: are broad-scale patterns of species richness robust to the violation of constant grain size?

Where distribution maps do not exist ecologists often use regional species lists to examine geographic patterns of species richness, despite the fact that inconsistent grain sizes across areas may complicate interpretation of the results. We compare patterns of species richness of European butterflies and dragonflies using regional species lists (varying grain size) and regular grids (constant grain size). We asked if species lists give results comparable to the gridded data when used in simple macroecological analysis of environmental correlates of species richness. We generated two equal-area grids (220 × 220 km and 440 × 440 km) to map the richness gradients and model species richness as a function of actual evapotranspiration (AET) and range in elevation. Then we used species checklists of 33 administrative regions of unequal sizes to construct the same environmental models while accounting for differences in area. Analysis of butterfly checklist data produced comparable results to the analysis of gridded data. In contrast, dragonfly checklist data had a distorted spatial pattern and much weaker associations with environmental variables than the gridded data. The robustness of checklist data appears to be variable, even within a single geographical region, and may not generate patterns congruent with those found using equal-area grids.     

How,and how much,natural cover loss increases species richness

Aim The species–area relationship has been applied in the conservation context to predict monotonic species richness declines as natural area is converted to human‐dominated land covers. However, some conversion of natural cover could introduce new habitat types and allow new open habitat species to occur. Moreover, decelerating richness–area relationships suggest that, as natural area is converted to human‐dominated covers, more species will be added to the rare habitat than are lost from the common one. Area effects and increased habitat diversity could each lead to a peaked relationship between species richness and the relative amount of natural area. The purpose of this study is to quantify the effect on avian species richness of conversion of natural area to human‐dominated land cover. Location Ontario, Canada. Methods We evaluated the responses of total avian richness, forest bird richness and open habitat bird richness to remaining natural area within 993 quadrats, each of 100 km². We quantified the amount of natural land cover and land‐cover heterogeneity using remote sensing data. We used structural equation modelling (SEM) to disentangle the relationships among avian richness, natural area and land‐cover heterogeneity. Results Spatial variation in avian richness was a peaked function of remaining natural area, such that losses of up to 44% of the natural area increased avian richness. This partly reflects increased variety of land cover; however, SEM suggests that much of the increase in richness is due to pure area effects. Richness of forest species declined by two species over this range of natural cover loss while open habitat bird richness increased by approximately 20 species. The effect of natural area on species richness is consistent with the sum of species–area curves for natural habitat species and human‐dominated habitat species. Main conclusions At least in northern temperate forests, almost half of the natural land cover can be converted to human‐dominated forms before avian richness declines. Conversion of < 50% of regional natural area to human‐dominated land cover can benefit open‐area species richness with relatively few losses of forest obligate species. However, with > 50% natural area conversion, species begin to drop out of regional assemblages.     

Iberian–Balearic fern regions and their explanatory variables

This article delineates the compositional regions present in the Iberian–Balearic fern flora and compares these regions to previously proposed biogeographic units. It also assesses the extent to which environmental variables could explain the regions and the fern species richness gradients found within them. A combination of 40 previously published and new maps were used to compile the distribution of 123 pteridophytes on a 50 × 50 km UTM grid. Cluster analysis of the resulting 257 squares was used to classify 10 regions based on fern species assemblages. Discriminant function analysis identified the environmental variables that best explained these fern composition regions. Using generalized linear models; the number of species in each square was regressed against topography, climate, geology, environmental diversity, land use and spatial variables within each region. Two main latitudinal pteridophyte zones can be recognized in the Iberian Peninsula. These two zones are longitudinally subdivided into two sub zones. The 10 regions established significantly differ both in species richness and influential environmental variables. Climatic variables discriminate the most among regions, followed by topography, heterogeneity and geology. Pteridophyte richness varies, with richer areas being located along the coast and the main mountain ranges and the poorest areas being in the central plateaus and some north eastern and south western river basins. Species richness variation in Iberia is positively correlated with altitude range, precipitation, maximum altitude and area with siliceous soils. It is negatively correlated with the total annual days of sun, however. The fact that species richness is explained by different variables within each of the 10 regions indicates that the specific factors determining the spatial distribution of species richness vary from region to region. Some coastal regions are poorly explained by the model, and display a negative correlation with the selected causal factors. This finding suggests that persistent historic effects might play a local role in determining species assemblages in these regions. An erratum to this article can be found at     

Broad-scale habitat use by red-legged partridge <Emphasis Type="Italic">(Alectoris rufa</Emphasis>) in a low-density area in northwestern Spain

We developed broad-scale habitat selection models for the distribution of red-legged partridge Alectoris rufa in a low-density area in northwestern Spain, the Baixa-Limia site of community importance (SCI). The fieldwork consisted of ground surveys in 1 × 1 km squares. For habitat selection analysis, we used a 2 × 2 km grid integrating the information obtained in the 1 × 1 km squares. As predictors we used environmental variables measured on digital 1:50,000 scale cartography using a geographical information system (GIS). The red-legged partridge was scarce in the study area. The logistic regression analysis carried out on data from the squares with probable and confirmed breeding included the area of scrubland and pastureland with a positive sign. Using the breeding index category (BIC) three variables produced a slightly positive response: area of scrubland and pastureland, length of border between scrublands and forests, and length of border between forests and dams. The difficulty for modelling the habitat selection of this species could be due to human activities (hunting, habitat loss, restocking of hunt species), and may have modified their habitat preferences. Furthermore, the occupation of suboptimal habitats would distort the real habitat preferences.     

Identification of the key ecological factors influencing vegetation degradation in semi-arid agro-pastoral ecotone considering spatial scales

When assessing re-vegetation project performance and optimizing land management, identification of the key ecological factors inducing vegetation degradation has crucial implications. Rainfall, temperature, elevation, slope, aspect, land use type, and human disturbance are ecological factors affecting the status of vegetation index. However, at different spatial scales, the key factors may vary. Using Helin County, Inner-Mongolia, China as the study site and combining remote sensing image interpretation, field surveying, and mathematical methods, this study assesses key ecological factors affecting vegetation degradation under different spatial scales in a semi-arid agro-pastoral ecotone. It indicates that the key factors are different at various spatial scales. Elevation, rainfall, and temperature are identified as crucial for all spatial extents. Elevation, rainfall and human disturbance are key factors for small-scale quadrats of 300 m × 300 m and 600 m × 600 m, temperature and land use type are key factors for a medium-scale quadrat of 1 km × 1 km, and rainfall, temperature, and land use are key factors for large-scale quadrats of 2 km × 2 km and 5 km × 5 km. For this region, human disturbance is not the key factor for vegetation degradation across spatial scales. It is necessary to consider spatial scale for the identification of key factors determining vegetation characteristics. The eco-restoration programs at various spatial scales should identify key influencing factors according their scales so as to take effective measurements. The new understanding obtained in this study may help to explore the forces which driving vegetation degradation in the degraded regions in the world.     

Habitat predictors of genetic diversity for two sympatric wetland‐breeding amphibian species

Population genetic diversity is widely accepted as important to the conservation and management of wildlife. However, habitat features may differentially affect evolutionary processes that facilitate population genetic diversity among sympatric species. We measured genetic diversity for two pond‐breeding amphibian species (Dwarf salamanders, Eurycea quadridigitata; and Southern Leopard frogs, Lithobates sphenocephalus) to understand how habitat characteristics and spatial scale affect genetic diversity across a landscape. Samples were collected from wetlands on a longleaf pine reserve in Georgia. We genotyped microsatellite loci for both species to assess population structures and determine which habitat features were most closely associated with observed heterozygosity and rarefied allelic richness. Both species exhibited significant population genetic structure; however, structure in Southern Leopard frogs was driven primarily by one outlier site. Dwarf salamander allelic richness was greater at sites with less surrounding road area within 0.5 km and more wetland area within 1.0 and 2.5 km, and heterozygosity was greater at sites with more wetland area within 0.5 km. In contrast, neither measure of Southern Leopard frog genetic diversity was associated with any habitat features at any scale we evaluated. Genetic diversity in the Dwarf salamander was strongly associated with land cover variables up to 2.5 km away from breeding wetlands, and/or results suggest that minimizing roads in wetland buffers may be beneficial to the maintenance of population genetic diversity. This study suggests that patterns of genetic differentiation and genetic diversity have associations with different habitat features across different spatial scales for two syntopic pond‐breeding amphibian species.     

The effect of land type diversity and spatial heterogeneity on farmland birds in Norway

The decline in farmland birds observed throughout Europe during recent decades has attracted much attention. Agricultural intensification or land abandonment are commonly forwarded as key drivers. Several countries have established agri-environmental schemes (AES) to counter these negative trends among farmland birds. This paper reports a study of the relationship between land use and bird species in the agricultural landscape of Norway. The main objective was to investigate the effect of spatial heterogeneity and diversity of land use on total richness and abundance of farmland birds at a national level.Monitoring the distribution and abundance of birds is part of the Norwegian monitoring programme for agricultural landscapes. The monitoring programme is based on mapping of 1 × 1 km squares distributed across the entire agricultural landscape. Within these squares permanent observation points are established for bird monitoring. Detailed interpretation of aerial photographs provides the land classification. We tested the relationship between landscape metrics at different levels of land type detail and species richness and abundance of farmland and non-farmland birds.There was a positive relationship between species richness and abundance of farmland birds and agricultural area. For non-farmland birds the relationship was negative. Spatial heterogeneity of land use was a significant positive factor for both farmland and non-farmland species. High land type diversity was positive for farmland bird richness, but negative for abundance. Non-farmland bird richness was not affected by land type diversity, but abundance had a negative response.The results presented in this paper highlight the importance of a spatial heterogeneous landscape. However, we also found that land type diversity could negatively affect the abundance of both farmland and non-farmland birds. Our findings suggest a need for different management approaches depending on whether the aim is increased species richness or abundance. Achieving both aims with the same means might be difficult. We thus suggest a need for land use analyses before proper management strategies can be implemented.     

Effects of spatial heterogeneity on butterfly species richness in Rocky Mountain National Park, CO, USA

We investigated butterfly responses to plot-level characteristics (plant species richness, vegetation height, and range in NDVI [normalized difference vegetation index]) and spatial heterogeneity in topography and landscape patterns (composition and configuration) at multiple spatial scales. Stratified random sampling was used to collect data on butterfly species richness from seventy-six 20 × 50 m plots. The plant species richness and average vegetation height data were collected from 76 modified-Whittaker plots overlaid on 76 butterfly plots. Spatial heterogeneity around sample plots was quantified by measuring topographic variables and landscape metrics at eight spatial extents (radii of 300, 600 to 2,400 m). The number of butterfly species recorded was strongly positively correlated with plant species richness, proportion of shrubland and mean patch size of shrubland. Patterns in butterfly species richness were negatively correlated with other variables including mean patch size, average vegetation height, elevation, and range in NDVI. The best predictive model selected using Akaike’s Information Criterion corrected for small sample size (AIC_c), explained 62% of the variation in butterfly species richness at the 2,100 m spatial extent. Average vegetation height and mean patch size were among the best predictors of butterfly species richness. The models that included plot-level information and topographic variables explained relatively less variation in butterfly species richness, and were improved significantly after including landscape metrics. Our results suggest that spatial heterogeneity greatly influences patterns in butterfly species richness, and that it should be explicitly considered in conservation and management actions.     

Productivity–species richness relationship changes from unimodal to positive linear with increasing spatial scale in the Inner Mongolia steppe

Productivity–species diversity relationships have been a controversial research topic in ecology with scale believed to be among the main reasons for discovering different relationships. We collected data on species diversity (richness) and productivity (peak above-ground biomass) of the Stipa breviflora association in the Inner Mongolia grassland to examine spatial scale dependency and possible underlying mechanisms responsible for the relationships found. One local and seven different landscape scales (the first level corresponds in extent to a 100 × 100 km area, which is increased consecutively by 100 km resulting in the 700 × 700 km area at the highest level) were considered. We found that: (1) unimodal relationships dominated the local scale, but this varied depending on the position along successional gradients; (2) a positive linear relationship was common at larger spatial scales; (3) biotic processes were the most likely primary factor underlying local scale unimodal relationships, but environmental heterogeneity (precipitation patterns) was the main determinant of relationships found at larger spatial scales; (4) our study contributed to other empirical evidence and predictions of theoretical models regarding scale dependency of productivity–species richness relationships; (5) while earlier research demonstrated positive linear species richness–productivity relationships across a number of ecological scales in the Inner Mongolia steppe, our study specifically tested a spectrum of geographical scales to confirm the scale-dependency of this relationship. Lastly, our study emphasized the critical role played by precipitation patterns in controlling biodiversity and grassland ecosystem functioning, which maintains the relatively high level of biodiversity and stable ecosystem processes.     

Relative contributions of local and regional factors to species richness and total density of butterflies and moths in semi-natural grasslands

Metapopulation theory predicts that species richness and total population density of habitat specialists increase with increasing area and regional connectivity of the habitat. To test these predictions, we examined the relative contributions of habitat patch area, connectivity of the regional habitat network and local habitat quality to species richness and total density of butterflies and day-active moths inhabiting semi-natural grasslands. We studied butterflies and moths in 48 replicate landscapes situated in southwest Finland, including a focal patch and the surrounding network of other semi-natural grasslands within a radius of 1.5 km from the focal patch. By applying the method of hierarchical partitioning, which can distinguish between independent and joint contributions of individual explanatory variables, we observed that variables of the local habitat quality (e.g. mean vegetation height and nectar plant abundance) generally showed the highest independent effect on species richness and total density of butterflies and moths. Habitat area did not show a significant independent contribution to species richness and total density of butterflies and moths. The effect of habitat connectivity was observed only for total density of the declining butterflies and moths. These observations indicate that the local habitat quality is of foremost importance in explaining variation in species richness and total density of butterflies and moths. In addition, declining butterflies and moths have larger populations in well-connected networks of semi-natural grasslands. Our results suggest that, while it is crucial to maintain high-quality habitats by management, with limited resources it would be appropriate to concentrate grassland management and restoration to areas with well-connected grassland networks in which the declining species currently have their strongest populations. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Diversity of European habitat types is correlated with geography more than climate and human pressure

Habitat richness, that is, the diversity of ecosystem types, is a complex, spatially explicit aspect of biodiversity, which is affected by bioclimatic, geographic, and anthropogenic variables. The distribution of habitat types is a key component for understanding broad‐scale biodiversity and for developing conservation strategies. We used data on the distribution of European Union (EU) habitats to answer the following questions: (i) how do bioclimatic, geographic, and anthropogenic variables affect habitat richness? (ii) Which of those factors is the most important? (iii) How do interactions among these variables influence habitat richness and which combinations produce the strongest interactions? The distribution maps of 222 terrestrial habitat types as defined by the Natura 2000 network were used to calculate habitat richness for the 10 km × 10 km EU grid map. We then investigated how environmental variables affect habitat richness, using generalized linear models, generalized additive models, and boosted regression trees. The main factors associated with habitat richness were geographic variables, with negative relationships observed for both latitude and longitude, and a positive relationship for terrain ruggedness. Bioclimatic variables played a secondary role, with habitat richness increasing slightly with annual mean temperature and overall annual precipitation. We also found an interaction between anthropogenic variables, with the combination of increased landscape fragmentation and increased population density strongly decreasing habitat richness. This is the first attempt to disentangle spatial patterns of habitat richness at the continental scale, as a key tool for protecting biodiversity. The number of European habitats is related to geography more than climate and human pressure, reflecting a major component of biogeographical patterns similar to the drivers observed at the species level. The interaction between anthropogenic variables highlights the need for coordinated, continental‐scale management plans for biodiversity conservation.