Butterflies are widely used to analyze biogeographical patterns, both at the global and regional scales. Thus far, most of the latter originated from well-surveyed northern regions, while the species-rich tropical areas lag due to a lack of appropriate data. We used checklists of 1379 butterfly species recorded in 36 federal states of the Republic of India (1) to explore the basic macroecological rules, and (2) to relate species richness and the distribution of endemics and geographic elements to geography, climate, land covers and socioeconomic conditions of the states. The area, land covers diversity and latitude did not affect species richness, whereas topographic diversity and the precipitation/temperature ratio (energy availability) were positive predictors. This is due the geographic and climatic idiosyncrasies of the Indian subcontinent, with its highest species richness in the small, densely forested mountainous northeast that receives summer monsoons. The peninsular effect that decreases the richness towards the tip of subcontinent is counterbalanced by the mountainous forested Western Ghats. Afrotropical elements are associated with savannahs, while Palearctic elements are associated with treeless habitats. The bulk of Indian butterfly richness, and the highest conservation priorities, overlap with global biodiversity hotspots, but the mountainous states of the Western Himalayas and the savannah states of peninsular India host distinctive faunas.

• Keywords
• Oriental realm, biogeographic elements, climate, faunal turnover, latitudinal gradient, peninsular effect,
• Publication type
• Journal Article MeSH

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.

• Keywords
• European habitat directive, anthropogenic impact, biodiversity conservation, environmental predictors, habitat richness, terrain ruggedness index,
• Publication type
• Journal Article MeSH

Vegetation complexity is an important predictor of animal species diversity. Specifically, taller vegetation should provide more potential ecological niches and thus harbor communities with higher species richness and functional diversity (FD). Resource use behavior is an especially important functional trait because it links species to their resource base with direct relevance to niche partitioning. However, it is unclear how exactly the diversity of resource use behavior changes with vegetation complexity. To address this question, we studied avian FD in relation to vegetation complexity along a continental-scale vegetation gradient. We quantified foraging behavior of passerine birds in terms of foraging method and substrate use at 21 sites (63 transects) spanning 3,000 km of woodlands and forests in Australia. We also quantified vegetation structure on 630 sampling points at the same sites. Additionally, we measured morphological traits for all 111 observed species in museum collections. We calculated individual-based, abundance-weighted FD in morphology and foraging behavior and related it to species richness and vegetation complexity (indexed by canopy height) using structural equation modeling, rarefaction analyses, and distance-based metrics. FD of morphology and foraging methods was best predicted by species richness. However, FD of substrate use was best predicted by canopy height (ranging 10-30 m), but only when substrates were categorized with fine resolution (17 categories), not when categorized coarsely (8 categories). These results suggest that, first, FD might increase with vegetation complexity independently of species richness, but whether it does so depends on the studied functional trait. Second, patterns found might be shaped by how finely we categorize functional traits. More complex vegetation provided larger "ecological space" with more resources, allowing the coexistence of more species with disproportionately more diverse foraging substrate use. We suggest that the latter pattern was driven by nonrandom accumulation of functionally distinct species with increasing canopy height.

• Keywords
• birds, foraging behavior, functional diversity, resource partitioning, species richness, vegetation complexity,
• Publication type
• Journal Article MeSH

Climate is a major driver of species diversity. However, its effect can be either direct due to species physiological tolerances or indirect, whereby wetter climates facilitate more complex vegetation and consequently higher diversity due to greater resource availability. Yet, studies quantifying both direct and indirect effects of climate on multiple dimensions of diversity are rare. We used extensive data on species distributions, morphological and ecological traits, and vegetation across Australia to quantify both direct (water availability) and indirect (habitat diversity and canopy height) effects of climate on the species richness (SR), phylogenetic diversity (PD), and functional diversity (FD) of 536 species of birds. Path analyses revealed that SR increased with wetter climates through both direct and indirect effects, lending support for the influence of both physiological tolerance and vegetation complexity. However, residual PD and residual FD (adjusted for SR by null models) were poorly predicted by environmental conditions. Thus, the FD and PD of Australian birds mostly evolved in concert with SR, with the possible exception of the higher-than-expected accumulation of avian lineages in wetter and more productive areas in northern and eastern Australia (with high residual PD), permitted probably by older biome age.

• MeSH
• Biodiversity * MeSH
• Ecology MeSH
• Ecosystem * MeSH
• Phylogeny MeSH
• Climate * MeSH
• Birds physiology   MeSH
• Plants MeSH
• Water * MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH
• Geographicals
• Australia MeSH
• Names of Substances
• Water * MeSH

Most of earth's biodiversity is comprised of interactions among species, yet it is unclear what causes variation in interaction diversity across space and time. We define interaction diversity as the richness and relative abundance of interactions linking species together at scales from localized, measurable webs to entire ecosystems. Large-scale patterns suggest that two basic components of interaction diversity differ substantially and predictably between different ecosystems: overall taxonomic diversity and host specificity of consumers. Understanding how these factors influence interaction diversity, and quantifying the causes and effects of variation in interaction diversity are important goals for community ecology. While previous studies have examined the effects of sampling bias and consumer specialization on determining patterns of ecological networks, these studies were restricted to two trophic levels and did not incorporate realistic variation in species diversity and consumer diet breadth. Here, we developed a food web model to generate tri-trophic ecological networks, and evaluated specific hypotheses about how the diversity of trophic interactions and species diversity are related under different scenarios of species richness, taxonomic abundance, and consumer diet breadth. We investigated the accumulation of species and interactions and found that interactions accumulate more quickly; thus, the accumulation of novel interactions may require less sampling effort than sampling species in order to get reliable estimates of either type of diversity. Mean consumer diet breadth influenced the correlation between species and interaction diversity significantly more than variation in both species richness and taxonomic abundance. However, this effect of diet breadth on interaction diversity is conditional on the number of observed interactions included in the models. The results presented here will help develop realistic predictions of the relationships between consumer diet breadth, interaction diversity, and species diversity within multi-trophic communities, which is critical for the conservation of biodiversity in this period of accelerated global change.

• MeSH
• Bayes Theorem MeSH
• Biodiversity * MeSH
• Models, Biological * MeSH
• Herbivory MeSH
• Linear Models MeSH
• Computer Simulation MeSH
• Food Chain * MeSH
• Plants MeSH
• Animals MeSH
• Check Tag
• Animals MeSH
• Publication type
• Journal Article MeSH
• Research Support, Non-U.S. Gov't MeSH