Patterns Of Bird Species Richness Research Articles

Seasonal changes in altitudinal patterns of bird species richness in a temperate low-elevation mountain

Mountain environments change rapidly over short distances along altitudinal gradients, providing an ideal system for exploring the mechanisms that shape biodiversity gradients. Species richness is the most studied diversity metric in mountains, and altitudinal patterns and their shaping mechanisms have been investigated worldwide. Although the altitudinal patterns of species richness in breeding bird assemblages have been extensively studied, less attention has been paid to seasonal changes in patterns between winter and summer. Furthermore, the effects of severe climate in high-elevation areas on seasonal changes in the altitudinal pattern of species richness in insular mountains remain unclear. We investigated changes in the pattern between the breeding and wintering seasons using field and literature surveys in Mount Tsukuba (877 m a.s.l.), central Japan. Temperatures at the middle elevations of the slope are relatively higher than those at the foot of the mountain in winter. The mountain is covered with forests up to its summit. We found that the altitudinal pattern of species richness was the low-plateau during the wintering season. Low-elevation areas were havens for wintering species, whereas high-elevation areas were impoverished in wintering species. Conversely, there was no association between elevation and species richness during the breeding season. Our study suggests that the relaxation of severe climates in high-elevation areas during winter and verdant forests in the highlands during summer are critical mechanisms driving seasonal changes in the altitudinal pattern of species richness. Furthermore, we highlight that comprehensive monitoring, including wintering seasons, is essential for detecting the changes in the diversity patterns of mountain bird assemblages due to the shift in the peak of wintering species richness under ongoing climate change.

Diversification in birds is promoted by plant diversity, topographic heterogeneity and stable paleoclimate

AbstractAimsLong‐term climate stability, contemporary climate and environmental heterogeneity have been linked to bird diversity patterns through their direct impacts on diversification rate, as well as through their indirect effects on plant species richness, which could also directly and indirectly affect bird diversity. This study aimed to quantitatively assess whether these potential drivers could indirectly affect bird species richness through their direct effects on the diversification rate in birds in eastern Asia.LocationMainland China.TaxonBirds.MethodsUsing the distribution data of 1127 bird species across 320 prefecture cities in China and a phylogenetic tree of these bird species, we analysed the distribution patterns of bird species richness and diversification rate of all birds, passerine birds and non‐passerine birds, respectively. We also investigated their relationships with long‐term climate stability, contemporary climate, elevation range and plant diversity using ordinary least squares regression model and simultaneous autoregressive model. In addition, structural equation model was used to analyse whether these drivers could indirectly affect bird species richness through their direct effects on bird diversification rate.ResultsThe results showed that bird species richness and diversification rate were highest in southwestern China. Additionally, diversification rate for passerine birds was higher than non‐passerine birds and all birds. Regression analyses revealed that plant species richness was the variable most associated with bird species richness and diversification rate across the three groups. Notably, results from structural equation model indicated that plant species richness, elevation range and glacial–interglacial climate change can indirectly influence bird species richness by directly affecting diversification rates.Main ConclusionsThese findings emphasize the importance of considering diversification rate when understanding the geographic distribution patterns of bird diversity. The direct and indirect effects of plant species richness, elevation range and glacial–interglacial climate change on bird diversity highlight the crucial role of mountain regions with higher plant diversity and stable paleoclimate in forming and maintaining biodiversity.

The impact of a massive wildfire event on avian species richness and abundance in an arid African savanna ecosystem

Fire shapes ecological diversity, but its influence on bird communities is poorly understood. Fire is often considered crucial to management of savanna systems. Here, we assess effects of fire on bird species richness and density after a wildfire in September 2021, in an arid South African savanna where fire is infrequent. Point counts initially surveyed in 2004 now occurred in burnt and unburnt habitats, and were revisited five times across seasons from December 2021 to January 2023. This enabled identification of interaction effects of change over time and with fire from linear models using site and survey as predictor variables in a Before-After Control-Impact (BACI) framework. Patterns of bird species richness were explored using subsets of repeated points and those of summer to autumn surveys. To explore fire impacts on bird density, we used ‘Distance Sampling’ using additional surveys. Although total species richness during counts did not differ between survey periods and burnt and unburnt areas, densities of all birds were ca. 50% higher in unburnt veld relative to burnt, with this difference driven mostly by granivores. Three of 24 common species were significantly negatively influenced by fire. Fires may profoundly affect this arid zone bird community, a finding that would be overlooked if only species richness had been considered, and densities not accounted for. Fire, although potentially positive for biodiversity in mesic savannas, may negatively impact multiple taxa in arid savannas, which have a very different evolutionary and ecological history to that of more mesic savannas. These differences must be considered in management strategies.

Are the same factors determining biodiversity in cities across different regions? Comparing drivers of urban bird richness patterns in Southern Asia vs. Western Europe

AbstractAccording to general urban ecological understanding, bird species richness declines in highly urban areas due to the increasing extent of built-up areas, and decreasing proportions of green areas. However, this hypothesis is mainly based on studies conducted in cities located in the Global North and rarely in the Global South. We aimed to assess whether existing conceptual understandings of drivers of bird species richness patterns within cities are similar across different regions, specifically Southern Asia (in the Global South) vs. Western Europe (in the Global North). Using avian occurrence data drawn from GBIF (gbif.org), we estimated resident bird species richness in 943 selected grid cells (each cell corresponding to approximately 1 km2 area) distributed in 24 cities, 13 in Southern Asia and 11 in Western Europe. We applied generalised linear mixed models to relate resident bird richness with different explanatory variables of gradients of urbanisation, habitat and climatic factors using the selected grid cells as sampling units. Our results showed that bird richness declined with increasing human built-up and settlement extent (imperviousness) in both regions, but this relationship was stronger in Southern Asia compared to Western Europe. Bird richness also sharply declined in urban areas located far from inland waterbodies, but only in Southern Asia. Our findings suggest that high imperviousness drives bird richness decline, but this process appears to be more pronounced in regions where rapid urbanisation is ongoing. Urban planning integrating green spaces throughout cities is crucial in Southern Asia, as it is in Western Europe, to benefit both people and biodiversity.

Multi-grain habitat models that combine satellite sensors with different resolutions explain bird species richness patterns best

Animals select habitat at multiple spatial scales, suggesting that biodiversity modeling, for example of species richness, should be based on environmental data gathered at multiple spatial scales, and especially multiple grain sizes. Different satellite sensors collect data at different spatial resolutions and therefore provide opportunities for multi-grain habitat measures. The dynamic habitat indices (DHIs), which are derived from satellite data, capture patterns of vegetative productivity and predict bird species richness well. However, the DHIs have only been analyzed at single resolutions (e.g., 1-km), and have not yet been derived from high-resolution satellite data (< 10 -m). Our goal was to predict bird species richness based on measures of vegetation productivity (DHIs, NDVI median and NDVI percentile 90th) across a range of spatial resolutions both from different sensors, and from resampled high-resolution imagery. We analyzed bird species richness within 215 forest, grassland and shrubland plots (56.25 ha) located at 26 terrestrial field sites of the National Ecology Observatory Network (NEON), in the continental US. To obtain our multi-resolution measures of vegetation productivity, we acquired data from Planetscope (3-m), RapidEye (5-m), Sentinel-2 (10-m), Landsat-8 (30-m) and MODIS (250-m) from 2017 to 2020, generated time series of NDVI, calculated the three DHIs (cumulative, minimum and variation), NDVI median and the 90th percentile NDVI and calculated 1st and 2nd order texture measures. We evaluated the performance of the derived measures to predict bird species richness of habitat specialist guilds based on (i) univariate models (ii) multivariate models with single-resolution measures and (iii) multivariate models with multi-resolution measures. Single-spatial resolution measures predicted bird species richness moderately well (R2 up to 0.51) and the best performing spatial resolution and measure differed among bird species guilds. High-spatial resolution (3–5 m) measures outperformed medium-resolution measures (10–250 m). Models for all guilds performed best when incorporating multiple resolutions, including for all species richness (R2 = 0.63) and for forest (R2 = 0.72), grassland (R2 = 0.53) and shrubland specialists (R2 = 0.46). In addition, models based on multi-resolution data from different sensors performed better than models based on resampled high-resolution data for any of the guilds. Our results highlight, first, the value of the DHIs derived from high-resolution satellite data to predict bird species richness and, second, that remotely-sensed vegetation productivity measures from multiple spatial resolutions offer great promise for quantifying biodiversity.

Vegetation structure from LiDAR explains the local richness of birds across Denmark.

Classic ecological research into the determinants of biodiversity patterns emphasised the important role of three-dimensional (3D) vegetation heterogeneity. Yet, measuring vegetation structure across large areas has historically been difficult. A growing focus on large-scale research questions has caused local vegetation heterogeneity to be overlooked compared with more readily accessible habitat metrics from, for example, land cover maps. Using newly available 3D vegetation data, we investigated the relative importance of habitat and vegetation heterogeneity for explaining patterns of bird species richness and composition across Denmark (42,394 km2 ). We used standardised, repeated point counts of birds conducted by volunteers across Denmark alongside metrics of habitat availability from land-cover maps and vegetation structure from rasterised LiDAR data (10 m resolution). We used random forest models to relate species richness to environmental features and considered trait-specific responses by grouping species by nesting behaviour, habitat preference and primary lifestyle. Finally, we evaluated the role of habitat and vegetation heterogeneity metrics in explaining local bird assemblage composition. Overall, vegetation structure was equally as important as habitat availability for explaining bird richness patterns. However, we did not find a consistent positive relationship between species richness and habitat or vegetation heterogeneity; instead, functional groups displayed individual responses to habitat features. Meanwhile, habitat availability had the strongest correlation with the patterns of bird assemblage composition. Our results show how LiDAR and land cover data complement one another to provide insights into different facets of biodiversity patterns and demonstrate the potential of combining remote sensing and structured citizen science programmes for biodiversity research. With the growing coverage of LiDAR surveys, we are witnessing a revolution of highly detailed 3D data that will allow us to integrate vegetation heterogeneity into studies at large spatial extents and advance our understanding of species' physical niches.

Limnological factors that affect waterbird assemblages in semi-arid reservoirs of Tigray National Regional State, northern Ethiopia

Published accounts of the conservation of biodiversity indicate that understanding patterns of species distribution and richness is crucial. However, what drives patterns of species composition in a landscape remains debatable. I examined the relationship between limnological characteristics of reservoirs, morpho-edaphic variables, biological variables, and patterns of bird species richness and distribution. Six limnological, three morpho-edaphic variables, and biological variables were recorded for 35 reservoirs and analyzed by multivariate statistical techniques. To investigate the most important explanatory factors influencing avian species richness and their distribution, redundancy analysis (RDA) was used. A total of 85 bird species from 54 genera, with a mean species richness 14.23 ± 6.72 (mean ± standard deviation) per reservoir, were recorded. The RDA analysis identified two significant RDA axes, and 34.4% of the variation in species richness is explained by environmental variation (R2adj = 0.34375; P < 0.001). Bird species richness was positively correlated with the surface area of reservoirs. I showed here that reservoir size and environmental heterogeneity were the important features that affect bird species richness, thus providing an important insight into the ecological relationship between waterbird species richness and the limnological characteristics of reservoirs. The strong positive correlation between species richness and both size and environmental variables underscores the importance of these reservoir features in the management of wildlife conservation. Large, environmentally heterogeneous reservoirs can support more species than small, environmentally homogeneous reservoirs because large, environmentally heterogeneous limnetic ecosystems can provide different resources for nesting, foraging, and roosting habitats for a diversified bird species. The result here also plays a role towards strengthening our knowledge of aquatic bird ecology and the natural history of African-Eurasian Migratory waterbirds.

Habitat-specific diversity in Central European birds

ABSTRACT Capsule Bird species richness was highest in forest and urban habitat types, lower in grassland and wetland, and lowest in cropland. Aims To investigate bird species richness patterns across different habitat types in Czechia, Central Europe. Methods Data from a national breeding bird monitoring scheme in Czechia, based on mapping of positions of individual birds along transects, were used to express the number of species in habitat polygons. Each polygon was represented by one of the eight habitat types (coniferous, mixed and deciduous forest, cropland, grassland and other open habitat types, urban habitat, and wetland) obtained by detailed country-wide vegetation mapping. Species richness of individual polygons was related to polygon habitat type and area by linear mixed effects models, taking the surrounding land cover composition into account. Results Bird species richness was highest in forest, as predicted, and respective forest habitat types did not differ from each other. Urban habitat hosted a similar number of species as forest. Species richness varied greatly between different open habitat types: cropland was the most species-poor of all the habitat types considered, whereas grassland and other types of open habitats hosted significantly more species, albeit fewer than forests, and did not differ from wetland. Slopes of species-area relationships in respective habitat types largely followed the patterns in species richness. Conclusions The observed patterns are partly driven by natural habitat characteristics, such as high vertical stratification of forest vegetation facilitating coexistence of a higher number of species. However, biogeography may also play a role, for example, and the relatively short time periods for colonization from Eastern European source areas may underpin lower bird species richness in grasslands. In addition, human interventions may drive the steep slope of the species-area relationship in forest, presumably caused by mosaic harvesting, as well as the shallow slope of this relationship in cropland and wetland, as a result of their intensive exploitation.

Ecological constraints on elevational gradients of bird species richness in Tajikistan

The avifauna in Tajikistan has been widely studied for the last century, but specific work on species richness pattern along elevation gradients in Tajikistan is rarely investigated. Here, we reported the first study of bird species richness (BSR) in the high-altitude mountain systems (Tien Shan and Pamir-Alay) of Tajikistan which are very sensitive to the recent climate changes. We aim to explore the relationship of BSR pattern with elevation gradient and to determine the potential drivers underlying the patterns. We collected occurrence data from field surveys, published articles, and open access websites to compile a list of bird species along elevational gradients across the whole country. The BSR was counted by 100 ​m elevational bands ranging from 294 ​m to 5146 ​m. The patterns of BSR were calculated separately for five groups: all breeding birds, Passeriformes, Non-Passeriformes, large elevational range species, and small elevational range species. We calculated ecological and climatic factors of planimetric area, mid-domain effect (MDE), habitat heterogeneity (HH), mean annual temperature (MAT), temperature annual range (TAR), annual precipitation (AP), normalized difference vegetation index (NDVI), human influence index (HII), and human disturbance (HD) in each elevational band. A combination of polynomial regression, Pearson's correlation, and general least squares model analyses were used to test the effects of these factors on the BSR. A unimodal distribution pattern with a peak at 750–1950 m was observed for all breeding birds. The similar pattern was explored for Passeriformes and Non-Passeriformes, while species with different elevational range sizes had different shapes and peak elevations. For all the breeding birds and Passeriformes, BSR was significantly related to spatial, climate and human influence factors, while BSR of Non-Passeriformes positively correlated with all the given factors. First, second and fourth range classes of birds were significantly correlated with human influence factors. Moreover, large-ranged species had positive correlations with the mid-domain effect and weakly with habitat heterogeneity. We found that area, MAT and AP were the main factors to explain the richness pattern of birds, and the species richness increases with these three factors increasing. Multiple factors such as area and climate explain 84% of the variation in richness. Bivariate and multiple regression analyses revealed a consistent influence of spatial and climate factors in shaping the richness pattern for nearly all bird groups.

Patterns of bird species richness explained by annual variation in remotely sensed Dynamic Habitat Indices

Bird species richness is highly dependent on the amount of energy available in an ecosystem, with more available energy supporting higher species richness. A good indicator for available energy is Gross Primary Productivity (GPP), which can be estimated from satellite data.Our question was how temporal dynamics in GPP affect bird species richness. Specifically, we evaluated the potential of the Dynamic Habitat Indices (DHIs) derived from MODIS GPP data together with environmental and climatic variables to explain annual patterns in bird richness across the conterminous United States. By focusing on annual DHIs, we expand on previous applications of multi-year composite DHIs, and could evaluate lag-effects between changes in GPP and species richness.We used 8-day GPP data from 2003 to 2013 to calculate annual DHIs, which capture three aspects of vegetation productivity: (1) annual cumulative productivity, (2) annual minimum productivity, and (3) annual seasonality expressed as the coefficient of variation in productivity. For each year from 2003 to 2013, we calculated total bird species richness and richness within six functional guilds, based on North American Breeding Bird Survey data.The DHIs alone explained up to 53% of the variation in annual bird richness within the different guilds (adjusted deviance-squared D2adj = 0.20–0.52), and up to 75% of the variation (D2adj = 0.28–0.75) when combined with other environmental and climatic variables. Annual DHIs had the highest explanatory power for habitat-based guilds, such as grassland (D2adj = 0.67) and woodland breeding species (D2adj = 0.75). We found some inter-annual variability in the explanatory power of annual DHIs, with a difference of 5–7 percentage points in explained variation among years in DHI-only models, and 3–7 points for models combining DHI, environmental and climatic variables. Our results using lagged year models did not deviate substantially from same-year annual models.We demonstrate the relevance of annual DHIs for biodiversity science, as effective predictors of temporal variation in species richness patterns. We suggest that the use of annual DHIs can improve conservation planning, by conveying the range of patterns of biodiversity response to global changes, over time.

Winter Habitat Indices (WHIs) for the contiguous US and their relationship with winter bird diversity

The seasonal dynamics of snow cover strongly affect ecosystem processes and winter habitat, making them an important driver of terrestrial biodiversity patterns. Snow cover data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra satellites can capture these dynamics over large spatiotemporal scales, allowing for the development of indices with specific application in ecological research and predicting biodiversity. Here, our primary objective was to derive winter habitat indices (WHIs) from MODIS that quantify snow season length, snow cover variability, and the prevalence of frozen ground without snow as a proxy for subnivium conditions. We calculated the WHIs for the full snow year (Aug-Jul) and winter months (Dec-Feb) across the contiguous US from 2003/04 to 2017/18 and validated them with ground-based data from 797 meteorological stations. To demonstrate the potential of the WHIs for biodiversity assessments, we modeled their relationships with winter bird species richness derived from eBird observations. The WHIs had clear spatial patterns reflecting both altitudinal and latitudinal gradients in snow cover. Snow season length was generally longer at higher latitudes and elevations, while snow cover variability and frozen ground without snow were highest across low elevations of the mid latitudes. Variability in the WHIs was largely driven by elevation in the West and by latitude in the East. Snow season length and frozen ground without snow were most accurately mapped, and had correlations with station data across all years of 0.91 and 0.85, respectively. Snow cover variability was accurately mapped for winter (r = 0.79), but not for the full snow year (r = −0.21). The model containing all three WHIs used to predict winter bird species richness patterns across the contiguous US was by far the best, demonstrating the individual value of each index. Regions with longer snow seasons generally supported fewer species. Species richness increased steadily up to moderate levels of snow cover variability and frozen ground without snow, after which it steeply declined. Our results show that the MODIS WHIs accurately characterized unique gradients of snow cover dynamics and provided important information on winter habitat conditions for birds, highlighting their potential for ecological research and conservation planning.

Ecological Limits as the Driver of Bird Species Richness Patterns along the East Himalayan Elevational Gradient.

Variation in species richness across environmental gradients results from a combination of historical nonequilibrium processes (time, speciation, extinction) and present-day differences in environmental carrying capacities (i.e., ecological limits affected by species interactions and the abundance and diversity of resources). In a study of bird richness along the subtropical east Himalayan elevational gradient, we test the prediction that species richness patterns are consistent with ecological limits using data on morphology, phylogeny, elevational distribution, and arthropod resources. Species richness peaks at midelevations. Occupied morphological volume is roughly constant from low elevations to midelevations, implying that more species are packed into the same space at midelevations compared with low elevations. However, variance in beak length and differences in beak length between close relatives decline with elevation, which is a consequence of the addition of many small insectivores at midelevations. These patterns are predicted from resource distributions: arthropod size diversity declines from low elevations to midelevations, largely because many more small insects are present at midelevations. Weak correlations of species mean morphological traits with elevation also match predictions based on resources and habitats. Elevational transects in the tropical Andes, New Guinea, and Tanzania similarly show declines in mean arthropod size and mean beak length and, in these cases, likely contribute to declining numbers of insectivorous bird species richness along these gradients. The results imply that conditions for ecological limits are met, although historical nonequilibrium processes are likely to also contribute to the pattern of species richness.

Patterns of bird species richness at two sampling scales in the Karoo biome of South Africa

Species richness has become the common currency of studies of biodiversity. Here we consider measures of species richness for the birds of the arid Karoo region of South Africa. We measured species richness at two scales: at the broad-scale using pentads (5 × 5′); and at the fine scale, using point counts to determine important landscape features. Point counts within randomly selected pentads were conducted throughout the two biomes that comprise the Karoo (Succulent and Nama). Features at points were used to model covariates of species richness at the fine scale, while the total number of unique species from counts was used as the dependent variable at the broad scale. We considered determinants of total species richness; and the subset of ten bird species endemic to the Karoo. Finally, we modelled covariates of presence for 100 of the most common bird species. We found increasing vegetation height and decreasing sand-cover best explain total species richness, while lower Prosopis sp cover and increasing altitude explained endemic species richness at the broad scale. At the finer scale, presence of water and farmsteads were associated with highest general species richness, but with lower numbers of endemics. The presence of water, a ‘green’ score, sand cover and topography were identified as the most important variables explaining presence of individual species, but often with contrasting effects between species. We conclude that patterns of endemic bird species richness were explained by different variables compared to total species richness. We expect this information will inform landholders, wildlife managers and conservation practitioners in this unique arid zone environment.

Landsat 8 TIRS-derived relative temperature and thermal heterogeneity predict winter bird species richness patterns across the conterminous United States

The thermal environment limits species ranges through its influence on physiology and resource distributions and thus affects species richness patterns over broad spatial scales. Understanding how temperature drives species richness patterns is particularly important in the context of global change and for effective conservation planning. Landsat 8's Thermal Infrared Sensor (TIRS) allows direct mapping of temperature at moderate spatial resolutions (100 m, downscaled by the USGS to 30 m), overcoming limitations inherent in coarse interpolated weather station data that poorly capture fine-scale temperature patterns over broad areas. TIRS data thus offer the unique opportunity to understand how the thermal environment influences species richness patterns. Our aim was to develop and assess the ability of TIRS-based temperature metrics to predict patterns of winter bird richness across the conterminous United States during winter, a period of marked temperature stress for birds. We used TIRS data from 2013-2018 to derive metrics of relative temperature and intra-seasonal thermal heterogeneity. To quantify winter bird richness across the conterminous US, we tabulated the richness only for resident bird species, i.e., those species that do not move between the winter and breeding seasons, from the North American Breeding Bird Survey, the most extensive survey of birds in the US. We expected that relative temperature and thermal heterogeneity would have strong positive associations with winter bird richness because colder temperatures heighten temperature stress for birds, and thermal heterogeneity is a proxy for thermal niches and potential thermal refugia that can support more species. We further expected that both the strength of the effects and the relative importance of these variables would be greater for species with greater climate sensitivity, such as small-bodied species and climate-threatened species (i.e., those with large discrepancies between their current and future distributions following projected climate change). Consistent with our predictions, relative temperature and thermal heterogeneity strongly positively influenced winter bird richness patterns, with statistical models explaining 37.3% of the variance in resident bird richness. Thermal heterogeneity was the strongest predictor of small-bodied and climate-threatened species in our models, whereas relative temperature was the strongest predictor of large-bodied and climate-stable species. Our results demonstrate the important role that the thermal environment plays in governing winter bird richness patterns and highlight the previously underappreciated role that intra-seasonal thermal heterogeneity may have in supporting high winter bird species richness. Our findings thus illustrate the exciting potential for TIRS data to guide conservation planning in an era of global change.

Tropical bird species richness is strongly associated with patterns of primary productivity captured by the Dynamic Habitat Indices

Biodiversity science and conservation alike require environmental indicators to understand species richness and predict species distribution patterns. The Dynamic Habitat Indices (DHIs) are a set of three indices that summarize annual productivity measures from satellite data for biodiversity applications, and include: a) cumulative annual productivity; b) minimum annual productivity; and c) variation in annual productivity. At global scales and in temperate regions the DHIs predict species diversity patterns well, but the DHIs have not been tested in the tropics, where higher levels of productivity lead to the saturation of many remotely sensed vegetation indices. Our goal was to explain bird species richness patterns based on the DHIs in tropical areas. We related the DHIs to species richness of resident landbirds for five guilds (forest, scrub, grassland, generalist, and all resident birds) based on a) species distribution model (SDM) maps for 217 species, and b) range map for 564 species across Thailand. We also quantified the relative importance of the DHIs in multiple regression models that included two measures of topography, and two climate metrics using multiple regression, best-subsets, and hierarchical partitioning analyses. We found that the three DHIs alone explained forest bird richness best (R2adj 0.61 for both SDM- and rangemap based richness; 0.15–0.54 for the other guilds). When combining the DHIs with topography and climate, the richness of both forest birds and all resident bird species was equally well explained (R2adj 0.85 and 0.67 versus 0.81 and 0.68). Among the three DHIs, cumulative annual productivity had the greatest explanatory power for all guilds based on SDM richness maps (R2adj 0.54–0.61). The strong relationship between the DHIs and bird species richness in Thailand suggests that the DHIs capture energy availability well and are useful in biodiversity assessments and potentially bird conservation in tropical areas.

Climate, human disturbance and geometric constraints drive the elevational richness pattern of birds in a biodiversity hotspot in southwest China

To improve our understanding of mechanisms underlying biodiversity patterns, and provide insights for conservation biologists, elevational pattern of bird species richness and its causes were studied in the Gaoligong Mountains, China. On the east slope of the southern part of Gaoligong Mountains (24.79°N-26.49°N, 98.65°E−98.93°E), we conducted field surveys of birds at each 300-m band from 700 to 3400 m a.s.l., and for the two bands from 3400 to 4000 m a.s.l., we obtained data from historical records. We obtained climatic recording data from local meteorological stations that were located in our study area and calculated the mean annual temperature and precipitation. We also calculated the Area, MDE (the mid-domain effect), NDVI (the normalized difference vegetation index), habitat heterogeneity and human disturbance for each 300-m band. We then used multiple regression analysis to test the explanatory power of different factors for the elevational richness patterns of overall, endemic, non-endemic, large-ranged, and small-ranged birds. A total of 277 breeding bird species were recorded. We found consistent hump-shaped elevational patterns of species richness with varied peaks for different groups. The richness of endemic birds peaked at higher elevation than non-endemic birds. Although none of the seven factors (Area, MDE, mean annual temperature, annual precipitation, NDVI, habitat heterogeneity, and human disturbance) showed consistent explanatory power among different species groups, temperature and human disturbance played important roles in shaping the richness patterns of most bird groups whereas MDE contributed to the richness pattern of large-ranged species. Our results highlight the need to increase conservation effort at the low elevation of this biodiversity hotspot where there is high richness of birds but intensive human land use. Furthermore, more studies are needed in this biodiversity hotspot to clarify the detailed influence of climate change on elevational distributions of birds.

Elevational patterns of bird species richness on the eastern slope of Mt. Gongga, Sichuan Province, China

BackgroundIn biological systems, biological diversity often displays a rapid turn-over across elevations. This defining feature has made mountains classic systems for studying the spatial variation in diversity. Because patterns of elevational diversity can vary among lineages and mountain systems it remains difficult to extrapolate findings from one montane region to another, or among lineages. In this study, we assessed patterns and drivers of avian diversity along an elevational gradient on the eastern slope of Mt. Gongga, the highest peak in the Hengduan Mountain Range in central China, and a mountain where comprehensive studies of avian diversity are still lacking.MethodsWe surveyed bird species in eight 400-m elevational bands from 1200 to 4400 m a.s.l. between 2012 and 2017. To test the relationship between bird species richness and environmental factors, we examined the relative importance of seven ecological variables on breeding season distribution patterns: land area (LA), mean daily temperature (MDT), seasonal temperature range (STR), the mid-domain effect (MDE), seasonal precipitation (SP), invertebrate biomass (IB) and enhanced vegetation index (EVI). Climate data were obtained from five local meteorological stations and three temperature/relative humidity smart sensors in 2016.ResultsA total of 219 bird species were recorded in the field, of which 204 were recorded during the breeding season (April–August). Species richness curves (calculated separately for total species, large-ranged species, and small-ranged species) were all hump-shaped. Large-ranged species contributed more to the total species richness pattern than small-ranged species. EVI and IB were positively correlated with total species richness and small-ranged species richness. LA and MDT were positively correlated with small-ranged species richness, while STR and SP were negatively correlated with small-ranged species richness. MDE was positively correlated with large-ranged species richness. When we considered the combination of candidate factors using multiple regression models and model-averaging, total species richness and large-ranged species richness were correlated with STR (negative) and MDE (positive), while small-ranged species richness was correlated with STR (negative) and IB (positive).ConclusionsAlthough no single key factor or suite of factors could explain patterns of diversity, we found that MDE, IB and STR play important but varying roles in shaping the elevational richness patterns of different bird species categories. Model-averaging indicates that small-ranged species appear to be mostly influenced by IB, as opposed to large-ranged species, which exhibit patterns more consistent with the MDE model. Our data also indicate that the species richness varied between seasons, offering a promising direction for future work.

Are Food and Habitat Resources Key Factors Determining Bird Species Richness at Broad Landscape-Scale in the Mainland of China?

This work aims to examine the spatial pattern of bird species richness at broad landscape-scale and to determine the key factors correlated to this pattern in the mainland of China. We divided the mainland of China was divided into 241 quadrats, 2° latitude by 2° longitude. The number of bird species occurring in each quadrat was counted based on available records. Plant species richness was also measured and net primary productivity estimated for each quadrat. Climatic data of each quadrat were based on 30-year records from 830 county’s meteorological stations. The results showed that bird species richness was significantly correlated to most factors examined. Factors of food, water and habitat resources such as plant species richness, primary productivity, annual mean precipitation, and longitude were most significantly related to bird richness in China. Other factors such as monthly mean temperature of January, frost-free period, minimum temperature, annual mean temperature, latitude showed somewhat indirect effects on bird species richness, i.e. specifically, they directly influenced plant richness and productivity, which then influenced bird richness. The maximum bird species richness occurs in the south of Yunnan province close to Yarlung Zangbo Grand Canyon probably in response to rich food resources, while the minimum plant species richness was found on Qinghai-Tibetan Plateau where food resources and habitats are limited. Based on our results, we suggest that the protection and development of food and habitat resources should be a priority to conserve bird diversity in China.

Patrones de distribución y zonas prioritarias para la conservación de la avifauna de la costa del Pacífico de Guerrero, México

We modeled the environmental suitability conditions associated to the presence of 370 bird species along the Pacific coast of Guerrero state. Based on distribution models, regional patterns of richness, endemism and the accumulation at-risk species of birds were obtained. We used a complementarity analysis both for resident and aquatic birds and parsimony analysis of endemicity was performed in order to identify priority sites for conservation. Knowledge of the birds of the study area is incomplete according to the richness estimator (ICE: 77% of the total). The distributional patterns of bird species richness and endemism were more concentrated in the central-western area of the study region in the mountainous areas, except in the westernmost portion that also presented a large number of species in the lower areas coinciding with some lacustrine systems. The complementarity analysis for terrestrial birds identified 6 cells, the same number of cells for aquatic birds, and in both cases together harbored 100% of all species that were modeled. The parsimony analysis showed 2 areas of endemism, and some cells included in these areas were also identified in the complementarity analysis; hence, these areas should receive a higher level of prioritization. The conservation areas obtained in this study suggest a connectivity from the center to the west of the study area, region that coincides with findings in other studies using species importance for conservation.

Pattern or process? Evaluating the peninsula effect as a determinant of species richness in coastal dune forests.

The peninsula effect predicts that the number of species should decline from the base of a peninsula to the tip. However, evidence for the peninsula effect is ambiguous, as different analytical methods, study taxa, and variations in local habitat or regional climatic conditions influence conclusions on its presence. We address this uncertainty by using two analytical methods to investigate the peninsula effect in three taxa that occupy different trophic levels: trees, millipedes, and birds. We surveyed 81 tree quadrants, 102 millipede transects, and 152 bird points within 150 km of coastal dune forest that resemble a habitat peninsula along the northeast coast of South Africa. We then used spatial (trend surface analyses) and non-spatial regressions (generalized linear mixed models) to test for the presence of the peninsula effect in each of the three taxa. We also used linear mixed models to test if climate (temperature and precipitation) and/or local habitat conditions (water availability associated with topography and landscape structural variables) could explain gradients in species richness. Non-spatial models suggest that the peninsula effect was present in all three taxa. However, spatial models indicated that only bird species richness declined from the peninsula base to the peninsula tip. Millipede species richness increased near the centre of the peninsula, while tree species richness increased near the tip. Local habitat conditions explained species richness patterns of birds and trees, but not of millipedes, regardless of model type. Our study highlights the idiosyncrasies associated with the peninsula effect—conclusions on the presence of the peninsula effect depend on the analytical methods used and the taxon studied. The peninsula effect might therefore be better suited to describe a species richness pattern where the number of species decline from a broader habitat base to a narrow tip, rather than a process that drives species richness.