An Untapped and Undocumented Butterfly Diversity in a Rapidly Urbanizing and Fragmenting Forest Habitat in Pokhara, Nepal: First Checklist and Implications for Conservation and Ecotourism

Butterflies are among the most effective bioindicators of climate change; however, their diversity in many rural areas of the Central Himalayas remains understudied. This study provides an assessment of butterfly diversity in the foothills of Bhorletar, Madhya Nepal Municipality, Lamjung District, Nepal, within an elevation range of 420-600 m. Conducted between July 2019 and January 2021, the survey involved opportunistic observations and photography of adult butterflies in their natural habitats, with sampling occurring six times each month. The study aimed to investigate the species composition, richness, and abundance of butterflies across the survey period and identify seasonal changes in species composition and richness. A total of 94,009 individuals across 226 species, 129 genera, and six families were documented. During this study, Halpe arcuata Evans, 1937 and Hasora taminatus bhavara Fruhstorfer, 1911 were recorded for the first time in Nepal. Additionally, Halpe filda Evans, 1949 and Ctenoptilum vasava vasava (Moore, [1866]) were recorded for only the second and third times, respectively, in Nepal, following a gap of approximately three decades. The most abundant species was Pieris canidia indica Evans, 1926 (Relative Abundance [RA] 2.55%), followed by Pseudozizeeria maha maha (Kollar, [1844]) (RA 2.13%). Species richness showed an annual bimodal distribution, peaking in April (180 species) and August (161 species), while the lowest richness was observed in January and February, with 68 and 75 species, respectively. Diversity indices included a Shannon-Wiener index of 4.71, Pielou's J index of 0.87, an effective number of species of 111.24, and Margalef's richness index of 19.65, indicating high species diversity with a well-balanced mix of species evenness and richness. This study offers the first peer-reviewed checklist of butterflies from Bhorletar, providing crucial baseline data for future research and conservation efforts, and highlights the remarkable seasonal and species diversity within the region.

The ability to predict spatial patterns of species richness using a few easily measured environmental variables would facilitate timely evaluation of potential impacts of anthropogenic and natural disturbances on biodiversity and ecosystem functions. Two common hypotheses maintain that faunal species richness can be explained in part by either local vegetation heterogeneity or primary productivity. Although remote sensing has long been identified as a potentially powerful source of information on the latter, its principal application to biodiversity studies has been to develop classified vegetation maps at relatively coarse resolution, which then have been used to estimate animal diversity. Although classification schemes can be delineated on the basis of species composition of plants, these schemes generally do not provide information on primary productivity. Furthermore, the classification procedure is a time- and labour-intensive process, yielding results with limited accuracy. To meet decision-making needs and to develop land management strategies, more efficient methods of generating information on the spatial distribution of faunal diversity are needed. This article reports on the potential of predicting species richness using single-date Normalized Difference Vegetation Index (NDVI) derived from Landsat Thematic Mapper (TM). We use NDVI as an indicator of vegetation productivity, and examine the relationship of three measures of NDVI—mean, maximum, and standard deviation—with patterns of bird and butterfly species richness at various spatial scales. Results indicate a positive correlation, but with no definitive functional form, between species richness and productivity. The strongest relationships between species richness of birds and NDVI were observed at larger sampling grains and extent, where each of the three NDVI measures explained more than 50% of the variation in species richness. The relationship between species richness of butterflies and NDVI was strongest over smaller grains. Results suggest that measures of NDVI are an alternative approach for explaining the spatial variability of species richness of birds and butterflies.

Determinants of elevational distribution of butterfly species richness and abundance in the tropics are poorly understood. Here we assess the combined effects, both additive and interactive, of seasonality and habitat structure on the elevational distribution of butterflies in the Uluguru Mountains, Tanzania. We sampled butterflies along a 1100 m elevational gradient that extended from 1540 to 2639 m using a time-constrained fixed-area method during the short to long rains and long to short rains transitions, and in habitat structure classified as closed or open. We used semi-parametric generalized linear mixed models to assess the relation between butterfly species richness or abundance, and seasonality, habitat structure, family and elevation. For all species combined, species richness declined with elevation in both open and closed habitats during the long to short rains transition. During the short to long rains transition, species richness displayed a mid-elevation peak across habitats. Among the three focal families (Nymphalidae, Papilionidae and Pieridae) similar patterns in the elevational distribution of species richness were observed. Species abundance declined or remained stable with elevation across seasons and habitat structure; the exception being species abundance in open habitat during the short to long rain transition and increased slightly with elevation. Abundance by family did not vary significantly by habitat structure or season. Our results indicate that seasonality and habitat structure shape species richness and abundance of butterflies along an elevational gradient in the Uluguru Mountains. These patterns are important for informing conservation actions because temperature as well as annual and seasonal variation in precipitation are predicted to increase in East Africa as a result of climate change, important determinants of seasonality, while habitat disturbance may increase due to a projected doubling in Tanzania’s population over the next 27 years.

One of the major determinants of species richness is the amount of energy available, often measured as primary productivity. Heterogeneity of environmental variables has also been found to influence species richness. Predicting species distributions across landscapes and identifying areas that have high species richness, or vulnerable groups of species, is useful for land management. Remotely sensed data may help identify such areas, with the Normalized Difference Vegetation Index (NDVI) providing an estimate of primary productivity. We examined the relationship between maximum productivity (NDVI), heterogeneity of productivity, and species richness of birds and butterflies at multiple spatial scales. We also explored relationships between productivity, functional guilds and residency groups of birds, and vagility classes of butterflies. Positive linear relationships between maximum NDVI and number of functional guilds of birds were found at two spatial scales. We also found positive linear relationships between maximum NDVI and species richness of neotropical migrant birds at two scales. Heterogeneity of NDVI, by contrast, was negatively associated with number of functional guilds of birds and species richness of resident birds. Maximum NDVI was associated with species richness of all butterflies and of the most vagile butterflies. No association was found between heterogeneity of NDVI and species richness of butterflies. In the Great Basin, where high greenness and availability of water correspond to areas of high species richness and maximum NDVI, our results suggest that NDVI can provide a reliable basis for stratifying surveys of biodiversity, by highlighting areas of potentially high biodiversity across large areas. Measures of heterogeneity of NDVI appear to be less useful in explaining species richness.

Aim Accurate inventories of biota are typically restricted to few locations within an extensive region. Accordingly, effective planning must involve some form of surrogate measures coupled with spatial modelling. We conducted a simultaneous comparison of models of both species richness and the number of rare species using three types of surrogates (indicator species, vegetation composition and structure, and topoclimate) as predictors. We evaluated each type of surrogate alone and in combination with others.Location Data for our analyses were collected from 1996–2004 in three adjacent mountain ranges in the central Great Basin (Lander and Nye counties, Nevada, USA), the Shoshone Mountains, Toiyabe Range and Toquima Range.Methods Data on species richness and species composition of butterflies and birds and measures of vegetation composition and structure were obtained in the field. Topoclimatic variables were derived by GIS from digital sources and satellite images. We used Poisson regression with Bayesian model averaging to predict species richness and the number of rare species. We compared the expected prediction success of all models on the basis of internal and external validation trials.Results Same‐taxon indicator species were the most accurate predictors of species richness and of the number of rare species of butterflies and birds. Cross‐taxon indicator species and topoclimate variables were reasonably accurate predictors of species richness of butterflies and birds and of the number of rare butterfly species. Although vegetation variables were more effective for predicting species richness and number of rare species of birds than of butterflies, they were the least accurate predictors overall.Main conclusions Although indicator species may provide the most accurate predictions of species richness, their practical value, like any surrogate measure, depends greatly on ecological considerations and land‐use context. In general, the ability to predict numbers of rare species based on any set of candidate predictors was weaker than the ability to predict species richness, which may result from the high degree of stochasticity that often characterizes distributions of rare species. Our statistical approach for objective examination of different candidate predictors can help ensure that selection of species‐richness surrogates in any system is scientifically reliable and cost‐effective.

Urbanization is the land-use process that most significantly impacts flora and fauna. We conducted a multiple city comparison of two taxa to assess whether richness patterns are similar across cities and taxa. This study aimed to examine the effects socioecological factors, namely open area size, socioeconomic status and the built-up cover, on species richness and composition of plants and butterflies in five adjacent Mediterranean cities in Israel’s coastal plain. Vegetation surveys were conducted in 170 open area sites in various urban settings. In 34 of them, the presence of butterfly species was also recorded. Mixed-effect generalized linear models were used to examine the site's characteristics effect on the species richness. The identity of each city was included as a random effect in the models. Results indicated that overall plant species richness increased with patch size, whereas butterfly richness was not associated with this factor. Plant and butterfly species richness in all categories decreased with the increase in building cover, except for endemic plant species. The results demonstrate the complex contribution of urban open area patches to the biodiversity of different taxa, being conditional on their size, surrounding built-up area and socioeconomic values.

The capital of China, Beijing, has a history of more than 800 years of urbanization, representing a unique site for studies of urban ecology. Urbanization can severely impact butterfly communities, yet there have been no reports of the species richness and distribution of butterflies in urban parks in Beijing. Here, we conducted the first butterfly survey in ten urban parks in Beijing and estimated butterfly species richness. Subsequently, we examined the distribution pattern of butterfly species and analyzed correlations between butterfly species richness with park variables (age, area and distance to city center), and richness of other bioindicator groups (birds and plants). We collected 587 individual butterflies belonging to 31 species from five families; 74% of the species were considered cosmopolitan. The highest butterfly species richness and abundance was recorded at parks located at the edge of city and species richness was significantly positively correlated with distance from city center (p < 0.05). No significant correlations were detected between the species richness and park age, park area and other bioindicator groups (p > 0.05). Our study provides the first data of butterfly species in urban Beijing, and serves as a baseline for further surveys and conservation efforts.

The geographic ranges of many species have shifted polewards and uphill in elevation associated with climate warming, leading to increases in species richness at high latitudes and elevations. However, few studies have addressed community‐level responses to climate change across the entire elevational gradients of mountain ranges, or at warm lower latitudes where ecological diversity is expected to decline. Here, we show uphill shifts in butterfly species richness and composition in the Sierra de Guadarrama (central Spain) between 1967–1973 and 2004–2005. Butterfly communities with comparable species compositions shifted uphill by 293 m (± SE 26), consistent with an upward shift of approximately 225 m in mean annual isotherms. Species richness had a humped relationship with elevation, but declined between surveys, particularly at low elevations. Changes to species richness and composition primarily reflect the loss from lower elevations of species whose regional distributions are restricted to the mountains. The few colonizations by specialist low‐elevation species failed to compensate for the loss of high‐elevation species, because there are few low‐elevation species in the region and the habitat requirements of some of these prevent them from colonizing the mountain range. As a result, we estimated a net decline in species richness in approximately 90% of the region, and increasing community domination by widespread species. The results suggest that climate warming, combined with habitat loss and other drivers of biological change, could lead to significant losses in ecological diversity in mountains and other regions where species encounter their lower latitudinal‐range margins.

Effects of land cover and climate on species richness of butterflies in boreal agricultural landscapes

Conserving woodland butterflies in managed forests: Both local and landscape factors matter

Abstract: Recent ecological studies suggest that the landscape context of native habitat remnants may significantly influence plant and animal abundance and distribution within those remnants. Other research has revealed a weak link between landscape context and native community composition. To understand the relative importance of local and regional habitat characteristics for grassland butterflies, we assessed butterfly community diversity in four types of grassland habitats surrounded by varying amounts of urban development near Boulder, Colorado ( U.S.A.). We recorded butterfly species abundance and composition in 66 grassland study plots on five sampling dates in 1999 and 2000. Grasslands were of four types: native shortgrass, native mixed grass, native tallgrass, and planted hayfields. Grasslands also varied in quality, determined by the abundance of native versus exotic plant species. We observed highly significant effects of grassland type on butterfly species richness and composition. For example, tallgrass plots supported significantly higher butterfly species richness than shortgrass plots ( p &lt; 0.01). Habitat quality also affected butterfly species richness and composition. Low‐quality plots generally supported fewer species than moderate‐ or high‐quality plots ( p &lt; 0.05). Landscape context—the percentage of urbanization in the surrounding landscape—did not significantly predict butterfly species richness or composition. Our observations suggest that for the grassland butterfly communities in our study, (1) grassland type was the primary determinant of species richness and composition, (2) habitat quality secondarily affected butterfly community diversity, and (3) landscape context did not significantly predict butterfly species composition. Our findings emphasize the importance of maintaining high‐quality grassland habitat to protect native butterfly diversity.

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.

The aim of this paper is to examine whether current and/or Pleistocene geography affect the species richness and composition of Tuscan archipelago butterflies. This archipelago is located between Tuscany (Italy) and Corsica (France). Faunistic data was obtained from the literature and surveys. Our data revealed that contemporary geography is the most important factor determining the species richness and faunal composition of Tuscan archipelago butterflies. Indeed, current area and isolation of the islands are the only predictors significantly correlated with species richness. Paleogeographic features of the archipelago are not significantly correlated with species richness. Multidimensional scaling revealed patterns similar to those reported for other living groups. Specifically, Capraia and Montecristo group together with Corsica, while Elba, Giglio, Pianosa, Gorgona, Giannutri and the fossil island of Monte Argentario group with the Tuscan mainland. Recent geography seems to affect the faunal composition. Indeed Mantel test indicates that the similarity in the faunal composition of the Tuscan Archipelago islands is mainly related to present-day island characteristics and their relative distance from Tuscany and Corsica. Our results are similar to those recently obtained for Aegean archipelago butterflies.

Little is known about the diversity of tropical animal communities in recently fire‐affected environments. Here we assessed species richness, evenness, and community similarity of butterflies and odonates in landscapes located in unburned isolates and burned areas in a habitat mosaic that was severely affected by the 1997/98 ENSO (El Niño Southern Oscillation) event in east Kalimantan, Indonesian Borneo. In addition related community similarity to variation in geographic distance between sampling sites and the habitat/vegetation structure Species richness and evenness differed significantly among landscapes but there was no congruence between both taxa. The species richness of butterflies was, for example, highest in sites located in a very large unburned isolate whereas odonate species richness was highest in sites located in a small unburned isolate and once‐burned forest. We also found substantial variation in the habitat/vegetation structure among landscapes but this was mainly due to variation between unburned and burned landscapes and variation among burned landscapes. Both distance and environment (habitat/vegetation) contributed substantially to explaining variation in the community similarity (beta diversity) of both taxa. The contribution of the environment was, however, mainly due to variation between unburned and burned landscapes, which contained very different assemblages of both taxa. Sites located in the burned forest contained assemblages that were intermediate between assemblages from sites in unburned forest and sites from a highly degraded slash‐and‐burn area indicating that the burned forest was probably recolonised by species from these disparate environments. We, furthermore, note that in contrast to species richness (alpha diversity) the patterns of community similarity (beta diversity) were highly congruent between both taxa. These results indicate that community‐wide multivariate measures of beta diversity are more consistent among taxa and more reliable indicators of disturbance, such as ENSO‐induced burning, than univariate measures.

Modelling butterfly species richness using mesoscale environmental variables: model construction and validation for mountain ranges in the Great Basin of western North America