Impacts of Climate Change on Lepidoptera Biodiversity in Iran: Insights from Long-Term Climate Data and GBIF Records

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Biodiversity metrics were analyzed, and Generalized Linear Model (GLM) analysis were used to identify key climatic and ecological drivers of family richness. Spatial analysis revealed a mean family richness of 5.89 (SD=7.13) and a mean Shannon-Wiener Index of 0.83 (SD=0.93) across ecoregions. Biodiversity hotspots, such as the Caspian Hyrcanian mixed forests, Zagros Mountains forest steppe, and Elburz Range forest steppe, exhibited consistently high family richness (19, 17, and 16 families, respectively) and balanced family compositions. In contrast, arid ecoregions, including the Registan-North Pakistan sandy desert and Baluchistan xeric woodlands suffered from insufficient sampling, limiting biodiversity assessments. Temporal analyses revealed forested regions had relatively complete accumulation curves while arid and semi-arid regions displayed uneven and incomplete sampling. Comparative analyses demonstrated that GBIF documented 27 unique families out of 70 families in observational records. Negative Binomial GLM showed that temperature positively influenced family richness, while higher precipitation negatively impacted family-level distribution. Comparative analysis between GBIF data and observational records revealed complementary strengths. GBIF provided broad-scale trends, while localized surveys uncovered specialist families often overlooked in global datasets. These findings emphasize the need to prioritize conservation efforts in biodiversity-rich ecoregions and addressing sampling gaps in underrepresented areas to mitigate climate change impacts on Lepidoptera in Iran. Climate change Lepidoptera biodiversity GBIF data Iran ecoregions biodiversity hotspots Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Implications for insect conservation Conservation efforts in Iran should prioritize biodiversity-rich ecoregions, particularly forested areas, which consistently exhibit high Lepidoptera family richness. Addressing sampling gaps in underrepresented ecoregions, such as arid and semi-arid regions, is equally important for enhancing biodiversity assessments and developing robust conservation strategies. Standardized data collection, alongside the integration of biodiversity records into platforms with advanced temporal and geospatial monitoring capabilities, such as GBIF, is crucial for capturing trends in both generalist and specialist Lepidoptera families. In addition, adaptive management strategies should prioritize mitigating climate change impacts of temperature and precipitation. Such measures will help preserve Lepidoptera, which are essential indicators of ecosystem health and climate resilience in Iran. Introduction Biodiversity in Iran faces significant challenges due to both anthropogenic pressures and climatic changes, threatening various ecosystems and species across the country. Notably, modern infrastructure development, including extensive road systems, has fragmented natural habitats, resulting in wildlife-vehicle collisions that further disrupt biodiversity (Jowkar et al. 2016). In addition, unsustainable practices such as over-grazing and over-cutting have led to soil degradation, transforming once productive rangelands into desert-like conditions, thereby jeopardizing the flora and fauna dependent on these ecosystems (Valizadeh 2010; Amiraslani and Dragovich 2011; Jowkar et al. 2016). The rapid increase in Iran's human population, which tripled from approximately 25 million in 1965 to 76–78 million by 2013, has compounded these issues by intensifying demand for land, water, and resources, further exacerbating the loss of biodiversity (Jowkar et al. 2016). Conserving biodiversity requires a comprehensive understanding of insect distributions and the identification of critical habitats and biodiversity hotspots (Myers et al. 2000; Dobson et al. 2006). In the case of Iran, despite its recognition as a global biodiversity hotspot, the family richness has not been thoroughly studied, particularly for groups such as Lepidoptera (Mittermeier et al. 2004). Iran’s Lepidoptera fauna is particularly significant, with at least 4,812 species recorded, 19.7% of which are endemic (Rajaei et al. 2023). However, the protection of these species faces considerable challenges, as the country's protected areas (PAs) often do not coincide with biodiversity hotspots, leaving critical areas, such as the Alborz and Zagros Mountains, under-protected (Rajaei et al. 2023; Noori et al. 2024). This mismatch between biodiversity-rich regions and PAs underscores the need for better management strategies to conserve species, particularly endemic and threatened Lepidoptera (Farashi and Shariati, 2017; Noori et al. 2021). Insects, particularly Lepidoptera, are highly sensitive to climate change, with temperature and precipitation acting as primary drivers of their distribution, phenology, and population dynamics (Crozier 2003; Hill et al. 2021). Climate change has already been observed to affect the abundance and geographical ranges of many Lepidoptera, with warmer conditions and altered precipitation patterns often leading to population declines and range contractions (Woiwod 1997; Parmesan 2006). The sensitivity of butterflies and moths to these changes makes them excellent indicators of broader ecological shifts, as they are able to reflect both direct and indirect effects of climate change, including alterations in species interactions and habitat availability (Warren et al. 2001; Wilson and Maclean 2011). While the general understanding of climate change impacts on Lepidoptera is growing, gaps remain in our knowledge of how these changes unfold across different regions. Studies using long-term data, such as the Butterfly Monitoring Scheme (BMS) in Europe, have provided valuable insights into how Lepidoptera populations are responding to climate variability (Brereton et al. 2006). However, in regions like Iran, where comprehensive biodiversity monitoring is still developing, such data are scarce, and the existing biodiversity records are often biased by uneven geographic and taxonomic coverage (Beck et al. 2014; Rocha-Ortega et al. 2021). The Global Biodiversity Information Facility (GBIF) has become a crucial resource in addressing these data gaps by providing extensive species occurrence data, though it too faces challenges in data representation, particularly for invertebrate taxa like Lepidoptera (Beck et al. 2013; Codata et al. 2020). This study seeks to address these challenges by leveraging the extensive and increasingly accessible GBIF dataset to analyze Lepidoptera biodiversity in Iran from 1993 to 2023. By integrating this data with long-term climate records from the ERA5-Land dataset, it was hypothesized that climate change has driven significant shifts in the distribution and diversity of Lepidoptera families across Iran’s ecoregions. In addition to examining the temporal and spatial dynamics of Lepidoptera biodiversity, this study also evaluates the utility of GBIF data as a tool for biodiversity monitoring. A comparison between GBIF occurrence data (1993–2022) and the most recent Catalogue of the Lepidoptera of Iran (Rajaei et al. 2023)—the most comprehensive and up-to-date reference on the country's Lepidoptera—was conducted. By cross-referencing family-level data from both sources, this study identifies discrepancies and gaps in biodiversity coverage. Furthermore, spatial overlap between GBIF occurrence records and national ecoregions was assessed through geospatial analysis, providing insights into how well GBIF data represents the ecological diversity across Iranian provinces. This evaluation of data quality and spatial representation contributes to a better understanding of the broader implications of using GBIF data for long-term biodiversity monitoring. Materials and Methods Study Area, Data Collection and Temporal Scope The study covered the entire area of Iran, which spans about 1,650,000 km² and includes all 31 provinces. The study boundaries were defined using Iran Shapefile data from the National Geographical Organization of Iran. A geo-referenced dataset of Lepidoptera records in Iran was retrieved from the Global Biodiversity Information Facility (GBIF) on 9 November 2024. This dataset included 3957 records from 1993 to 2022, compiled from seven datasets provided by publishers from seven different countries (GBIF.org, 2024). The analysis focused on biodiversity trends at the family level. To ensure accuracy, spatial mapping was done using Python 3.11 within Iran Ecoregions specifically designed for Iran, aligning with its geographical and ecological features. Iran Ecoregions Shapefile Creation The Iran Ecoregions Shapefile was crafted using Python 3.11 libraries. Initially, the Iran shapefile (National Geographical Organization of Iran) and the global ecoregions shapefile sourced from the Terrestrial Ecoregions of the World dataset (Olson et al. 2001) were read using Geopandas. Subsequently, the ecoregions GeoDataFrame was reprojected to align with the Coordinate Reference System (CRS) of the Iran GeoDataFrame. Geopandas was then employed to conduct a spatial join between the Iran shapefile and ecoregions shapefile, integrating geographical attributes. The ecoregions were clipped to Iran's boundary, refining the dataset's spatial extent. The resulting Iran Ecoregions Shapefile (Fig. 1) was utilized for diversity analyses. Long-Term Climate Data Collection and Processing Temperature and precipitation data were obtained from the ERA5-Land reanalysis dataset, provided by the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (Muñoz Sabater 2019). Data spanning the years 1993 to 2022 were downloaded programmatically using Python 3.11 and the cdsapi library, which facilitated automated requests to the CDS API. The geographic focus was limited to the extent of Iran, defined by latitude (25°N–40°N) and longitude (44°E–63°E). Both datasets were retrieved in NetCDF format. For temperature, the monthly averaged 2-meter air temperature variable was converted from Kelvin to Celsius. Monthly data were then averaged to produce yearly mean temperature grids. For precipitation, the total precipitation variable was converted from meters (as provided by ERA5) to millimeters. Monthly totals were summed to calculate yearly precipitation grids. All calculations were performed using the Python 3.11 xarray library. The resulting yearly datasets for temperature and precipitation were aggregated into separate datasets spanning all years. These datasets retained spatial dimensions (latitude and longitude) and incorporated a temporal dimension (year). Spatial Biodiversity Analysis Spatial Distribution To evaluate Lepidoptera biodiversity across Iranian ecoregions, Python 3.11 was utilized along with the libraries including pandas, geopandas, matplotlib, shapely, and numpy for spatial data manipulation, analysis, and visualization. GBIF occurrence data for Lepidoptera families, containing complete latitude, longitude, and family classification information, was loaded as a GeoDataFrame in the WGS84 CRS and overlaid with Iranian ecoregion boundaries using shapefiles in geopandas. The ecoregion boundaries were clipped to match Iran’s outline, providing a precise national boundary. A spatial join was conducted to associate each occurrence point with an ecoregion, allowing family richness (the count of unique families) to be calculated for each ecoregion. Family richness values were merged with ecoregion polygons, and heatmaps were generated to illustrate biodiversity distribution. Biodiversity Metrics Calculation To evaluate Lepidoptera biodiversity across Iran's terrestrial ecoregions, Family Richness, the Shannon-Wiener Index, and descriptive statistics were calculated using occurrence data from the GBIF. Initially, ecoregion shapefiles and Lepidoptera occurrence records, which included family-level taxonomy and geographic coordinates, were imported into a Geographic Information System. A spatial join was conducted to link each occurrence record to its respective ecoregion, excluding "Lake" ecoregions to focus exclusively on terrestrial habitats. For each terrestrial ecoregion, Family Richness was calculated as the number of unique Lepidoptera families observed. The Shannon-Wiener Index was then calculated to assess biodiversity, taking into account both family richness and evenness within each ecoregion. This index was computed using the formula: H′= -∑(p i ⋅ln(p i )) where: H′ represents the Shannon-Wiener Index, an indicator of biodiversity, p i is the relative frequency (proportion) of occurrences of each family within the ecoregion, calculated as the number of occurrences of family divided by the total occurrences across all families in that ecoregion. Finally, descriptive statistics, including mean, standard deviation, and range, were computed for both metrics to summarize patterns across ecoregions. The biodiversity metrics and descriptive statistics were saved to an Excel file for subsequent analysis. Temporal Trends in Family Richness and Turnover Rate To assess long-term biodiversity trends in Lepidoptera across Iran's ecoregions, family richness and turnover rate metrics were calculated using data from GBIF occurrence records. Family richness, defined as the count of unique families observed per year within each ecoregion, was derived by grouping records by year and ecoregion and counting unique family entries, following established biodiversity assessment methods. Turnover rate, indicating changes in family composition over consecutive years, was calculated by comparing the sets of families observed in two consecutive years for each ecoregion. This rate was computed as the ratio of the number of families that appeared or disappeared between years to the total families observed across both years, highlighting annual shifts in family presence. Non-terrestrial ecoregions, such as "Lake," were excluded to focus on terrestrial biodiversity patterns. Temporal trends were visualized using Seaborn’s FacetGrid, which enabled the generation of line plots for family richness and turnover rate over time across ecoregions with observed Lepidoptera. Ecoregions with zero values in richness or turnover were excluded to maintain clarity. All plot elements, including titles and labels, were standardized in Times New Roman font for readability. Family Accumulative Curves Family accumulative curves were constructed to analyze long-term trends in Lepidoptera biodiversity across Iran’s ecoregions. Occurrence data spanning 1993–2022 was spatially assigned to ecoregions using the Iran Ecoregions shapefile, derived from the Terrestrial Ecoregions dataset (Olson et al. 2001). For each ecoregion, the cumulative number of unique families was calculated annually, with previously recorded families retained and newly observed families incrementally added each year. Ecoregion-specific accumulation curves were plotted using Python 3.11 utilizing distinct colors to distinguish patterns visually. Statistical Modeling of Biodiversity and Climate Factors To assess the long-term biodiversity of Lepidoptera across Iran’s ecoregions in relation to climate factors, a Generalized Linear Model (GLM) with a Negative Binomial link function was applied. Occurrence data were obtained from GBIF, encompassing records spanning three decades with geographic coordinates (latitude and longitude). Data processing and analysis were performed using Python 3.11, with libraries including pandas for tabular data manipulation, geopandas for spatial operations, statsmodels for statistical modeling, and numpy for numerical computations. Occurrences were spatially integrated with ecoregion shapefiles to assign records to specific ecoregions. Family richness, representing the count of unique Lepidoptera families per ecoregion-year, was selected as the response variable. Predictor variables included mean annual temperature, total annual precipitation, and sampling intensity. Climate variables were derived from georeferenced NetCDF files, averaged across ecoregions using xarray with spatial masking. All predictors were standardized using the StandardScaler function from sklearn to facilitate comparability and interpretability. The GLM was implemented using the glm function from statsmodels, with ecoregion included as a fixed effect to account for geographic variability. Model performance was evaluated through parameter significance, likelihood ratios, and residual diagnostics. Evaluation of GBIF Data vs. Observational Data A comparison between GBIF occurrence data (1993–2022) and the latest available Catalogue of the Lepidoptera of Iran was conducted to evaluate biodiversity coverage. The Catalogue of the Lepidoptera of Iran (Rajaei et al. 2023), as the first modern and comprehensive resource on Iranian Lepidoptera, provided national coverage for the entire order that synthesized all presently-known phylogenetic, systematic, taxonomic, nomenclatural, and geographic information (province level) on Iranian Lepidoptera (Rajaei and Karsholt 2023). The list of unique Lepidoptera families was extracted from GBIF occurrence data, which spans 1993 to 2022 and includes georeferenced records across various provinces and ecoregions. Each family name was cross-checked between the GBIF dataset and the Catalogue to identify discrepancies and gaps in biodiversity coverage. To evaluate the spatial overlap between ecoregions and provinces in Iran, geospatial analysis was performed using Python 3.11 GeoPandas library. Ecoregion and provincial boundary shapefiles for Iran were loaded, their CRS aligned, and the ecoregions clipped to Iran's national boundary. Spatial intersections were calculated to determine the percentage of area each province covers within an ecoregion. Provinces encompassing between 20% and 100% of an ecoregion’s area were recorded. Results and Discussion Spatial Distribution The analysis of Lepidoptera biodiversity across Iran's terrestrial ecoregions, as represented in Fig. (2), revealed significant variation in family richness and diversity levels among different habitats. Ecoregions such as the Caspian Hyrcanian mixed forests, Central Persian desert basins, and Zagros Mountains forest steppe exhibited the highest family richness, indicating that these areas likely support diverse and favorable environmental conditions for Lepidoptera. In contrast, several ecoregions, including the Registan-North Pakistan sandy desert, Baluchistan xeric woodlands, and Arabian Desert xeric shrublands, showed zero family richness, suggesting limited suitability for Lepidoptera due to extreme or resource-scarce conditions (Fig. 2). The observed variation in Lepidoptera family richness across Iran's terrestrial ecoregions highlights both the ecological significance of mountainous habitats and the vulnerability of biodiversity to climatic and anthropogenic pressures. The Caspian Hyrcanian mixed forests, Central Persian desert basins, and Zagros Mountains forest steppe emerged as biodiversity hotspots, aligning with findings by Noori et al. (2024) and Rajaei et al. (2023), which emphasized the role of the Alborz and Zagros mountain ranges as centers for Lepidoptera richness and endemism. These regions, characterized by complex topography and microhabitats, act as refugia, enabling species to persist through historical climatic fluctuations (Harrison and Noss 2017). In contrast, the limited or zero richness in arid ecoregions such as the Registan-North Pakistan sandy desert and Baluchistan xeric woodlands underscores the sensitivity of Lepidoptera to extreme abiotic conditions, consistent with studies showing the dependence of these ectothermic organisms on temperature and moisture availability (Kingsolver et al. 2011; Hill et al. 2021). Human activities, such as overgrazing, habitat fragmentation, and land-use changes, further exacerbate these patterns (Amiraslani and Dragovich 2011; Jowkar et al. 2016). Moreover, climate change-induced shifts in temperature and precipitation could drive range redistributions and local extinctions of vulnerable insects in these already resource-limited regions (Crozier 2003; Lenoir et al. 2020). Family-Level Biodiversity Across Ecoregions Table (1) summarizes Lepidoptera family richness and occurrences across the ecoregions of Iran from 1993 to 2022. Ecoregions with higher sampling intensity, such as the Caspian Hyrcanian mixed forests and Zagros Mountains forest steppe, were characterized by greater family richness, with 19 and 17 families recorded, respectively. Conversely, regions like the Registan-North Pakistan sandy desert reported only a few occurrences, reflecting incomplete sampling efforts that may have led to underrepresentation of Lepidoptera diversity. The data underscore the critical role of comprehensive and uniform sampling across all regions of Iran in obtaining accurate biodiversity assessments. Ecoregions with more extensive sampling, such as the Elburz Range forest steppe and Central Persian desert basins, exhibited higher family richness. In contrast, sparsely sampled regions, including the Tigris-Euphrates marshes and Mesopotamian shrub desert, were underrepresented, emphasizing the need to address sampling bias. Without such corrections, biodiversity estimates could underestimate richness in less-sampled regions. Table 1. Lepidoptera family richness and occurrence summary of Iran ecoregions (1993-2022) Ecoregion Total Number of Occurrences Total Number of Unique Families List of Unique Families Arabian Desert and East Sahero-Arabian xeric shrublands 1 0 - Azerbaijan shrub desert and steppe 2 0 - Badghyz and Karabil semi-desert 1 0 - Baluchistan xeric woodlands 1 0 - Caspian Hyrcanian mixed forests 3880 19 Noctuidae, Geometridae, Nymphalidae, Crambidae, Pieridae, Erebidae, Lycaenidae, Zygaenidae, Cossidae, Sphingidae, Notodontidae, Papilionidae, Hesperiidae, Pyralidae, Sesiidae, Depressariidae, Lasiocampidae, Tineidae, Gelechiidae Caspian lowland desert 5 3 Geometridae, Lasiocampidae, Lycaenidae Central Persian desert basins 9586 16 Geometridae, Noctuidae, Saturniidae, Nymphalidae, Pieridae, Lycaenidae, Psychidae, Hesperiidae, Papilionidae, Sphingidae, Plutellidae, Erebidae, Pyralidae, Tischeriidae, Crambidae, Zygaenidae Eastern Anatolian montane steppe 220 10 Geometridae, Sesiidae, Nymphalidae, Noctuidae, Papilionidae, Crambidae, Pterophoridae, Erebidae, Pieridae, Lycaenidae Elburz Range forest steppe 12919 16 Geometridae, Noctuidae, Nymphalidae, Lycaenidae, Hesperiidae, Pieridae, Sphingidae, Papilionidae, Erebidae, Pyralidae, Tineidae, Sesiidae, Zygaenidae, Crambidae, Drepanidae, Saturniidae Kopet Dag semi-desert 2 1 Papilionidae Kopet Dag woodlands and forest steppe 78 6 Geometridae, Nymphalidae, Sphingidae, Lycaenidae, Erebidae, Pieridae Kuh Rud and Eastern Iran montane woodlands 690 11 Geometridae, Noctuidae, Nymphalidae, Pieridae, Lycaenidae, Erebidae, Notodontidae, Hesperiidae, Sphingidae, Sesiidae, Zygaenidae Mesopotamian shrub desert 2 0 - Middle East steppe 1 0 - Registan-North Pakistan sandy desert 2 0 - South Iran Nubo-Sindian desert and semi-desert 3632 12 Pieridae, Nymphalidae, Geometridae, Lycaenidae, Erebidae, Noctuidae, Notodontidae, Sphingidae, Pterophoridae, Hesperiidae, Papilionidae, Plutellidae Tigris-Euphrates alluvial salt marsh 3 0 - Zagros Mountains forest steppe 14301 17 Geometridae, Crambidae, Nolidae, Nymphalidae, Papilionidae, Lycaenidae, Cossidae, Erebidae, Sesiidae, Pieridae, Hesperiidae, Sphingidae, Noctuidae, Pyralidae, Tineidae, Meessiidae, Zygaenidae The family-level biodiversity results across Iran's ecoregions emphasize the influence of both ecological factors and sampling bias on Lepidoptera diversity assessments. Regions such as the Caspian Hyrcanian mixed forests and Zagros Mountains forest steppe, which reported the highest family richness (19 and 17 families, respectively), align with prior studies highlighting these mountainous areas as biodiversity hotspots for Lepidoptera due to their ecological complexity, microhabitat heterogeneity, and historical role as refugia (Harrison and Noss 2017; Rajaei et al. 2023; Noori et al. 2024). These findings are consistent with broader global patterns, where mountainous and forested landscapes support higher insect diversity by acting as barriers and corridors for gene flow, facilitating both dispersal and isolation (Antonelli 2017; Rahbek et al. 2019). Conversely, ecoregions with minimal occurrences, such as the Registan-North Pakistan sandy desert and Baluchistan xeric woodlands, reflect not only extreme environmental conditions but also incomplete and geographically uneven sampling efforts, a challenge well-documented in biodiversity studies relying on opportunistic data (Fattorini 2013; Rocha-Ortega et al. 2021). This sampling bias can underestimate richness and limit our ability to identify critical conservation areas, particularly in regions where Lepidoptera populations remain underexplored. Addressing these biases through standardized monitoring, such as leveraging tools like the GBIF and novel methodologies like environmental DNA (eDNA) metabarcoding, is essential for filling data gaps and improving biodiversity estimates (Beck et al. 2013; Montgomery et al. 2021). Ultimately, the observed disparity in family richness underscores the urgent need for targeted surveys and conservation strategies in underrepresented regions to ensure comprehensive biodiversity management and protection of Lepidoptera diversity in Iran. Temporal Trends in Family Richness and Turnover Rates Temporal analyses revealed distinct trends in family richness across the study period (1993–2022). The Caspian Hyrcanian mixed forests and Zagros Mountains forest steppe consistently exhibited higher family richness, with notable peaks of 10 unique families in 2016 and 8 families in 2021, respectively (Fig. 3). Other regions, like the Central Persian desert basins and Kopet Dag woodlands, showed fluctuating yet generally lower richness levels. Periods with zero recorded family richness in certain ecoregions reflect either a lack of observations or incomplete data. Annual turnover rates further elucidated dynamic shifts in family composition across ecoregions (Fig. 4). Ecoregions such as the Elburz Range forest steppe and South Iran Nubo-Sindian desert exhibited high turnover rates in specific years, indicating substantial changes in family composition potentially driven by environmental variability or sampling inconsistencies. Conversely, ecoregions like Kuh Rud and Eastern Iran montane woodlands maintained relatively stable family compositions over time, as reflected by turnover rates close to 0.0. The temporal trends in Lepidoptera family richness and turnover rates across Iranian ecoregions reflect both ecological dynamics and the limitations of available data. The consistently high family richness observed in the Caspian Hyrcanian mixed forests and Zagros Mountains forest steppe aligns with their recognized role as biodiversity hotspots, characterized by favorable environmental conditions, microhabitat heterogeneity, and historical stability that support species persistence and diversity (Rajaei et al. 2023; Noori et al. 2024). Peaks in family richness, such as those recorded in 2016 and 2021, may correspond to years with improved sampling efforts or favorable climatic conditions that positively influenced Lepidoptera populations, as these ectothermic organisms are highly sensitive to temperature and moisture variability (Bale et al. 2002; Kingsolver et al. 2011). In contrast, regions like the Central Persian desert basins and Kopet Dag woodlands exhibited lower and fluctuating richness, consistent with findings that arid and semi-arid habitats often limit insect diversity due to extreme abiotic factors and resource scarcity (Woiwod 1997; Amiraslani and Dragovich 2011). The observed high turnover rates in ecoregions such as the Elburz Range forest steppe and South Iran Nubo-Sindian desert highlight significant changes in family composition, potentially driven by climatic fluctuations, habitat alterations, or sampling inconsistencies over time. These findings underscore the vulnerability of Lepidoptera to environmental variability, as changes in temperature, rainfall patterns, and habitat availability can directly influence family distributions, population dynamics, and interspecific interactions (Crozier 2003; Lenoir et al. 2020; Hill et al. 2021). Conversely, stable turnover rates in regions like the Kuh Rud and Eastern Iran montane woodlands suggest a degree of ecological resilience, where family composition remains relatively constant despite temporal variability. However, periods with zero recorded family richness in some ecoregions highlight the persistent challenges of incomplete or geographically biased sampling, which can obscure true biodiversity patterns and trends (Fattorini 2013; Rocha-Ortega et al. 2021). Addressing these data gaps through long-term, standardized monitoring programs and leveraging advanced biodiversity tools, such as GBIF and remote sensing technologies, is essential for accurately assessing Lepidoptera richness, turnover dynamics, and their responses to environmental change. Cumulative Family Richness Trends Cumulative trends over the 30-year study period revealed significant variation in family richness and sampling completeness across ecoregions (Fig. 5). The Caspian Hyrcanian mixed forests exhibited the highest cumulative family richness, reaching 19 families by 2022, with consistent additions over time. Ecoregions like the Zagros Mountains forest steppe and Central Persian desert basins also demonstrated gradual increases, reaching 17 families each. In contrast, arid regions such as the Arabian Desert and Registan-North Pakistan sandy desert showed no observed family richness, likely due to either limited Lepidoptera presence or insufficient sampling efforts. Sampling completeness varied markedly among ecoregions. The Caspian Hyrcanian mixed forests and Elburz Range forest steppe displayed relatively complete accumulation curves, while arid and semi-arid regions like the South Iran Nubo-Sindian desert showed uneven sampling efforts. Many desert and semi-desert ecoregions demonstrated no observed family richness, emphasizing the uneven distribution of sampling efforts across Iran. Biodiversity Metrics and Statistical Trends The statistical summary of biodiversity data revealed that for the ecoregions analyzed, the mean family richness was 5.89 with a standard deviation of 7.13, ranging from 0 to 19. The Shannon-Wiener Index, which measured biodiversity, had a mean of 0.83 and a standard deviation of 0.93, with values ranging from 0 to 2.21. Descriptive statistics for biodiversity metrics provide further insights into overall trends (Table 2). The mean family richness across all ecoregions was relatively low, reflecting limited Lepidoptera diversity in many regions. The Shannon-Wiener Index values were aligned with family richness trends, with the highest values observed in the Caspian Hyrcanian mixed forests and Central Persian desert basins. These regions not only supported high family richness but also exhibited balanced family compositions. The standard deviation for both metrics was high, indicating substantial variability in biodiversity across ecoregions. Table 2. Biodiversity metrics of lepidoptera families across Iran ecoregions Ecoregion Family Richness Shannon Wiener Index South Iran Nubo-Sindian desert and semi-desert 12 2.031745536 Kuh Rud and Eastern Iran montane woodlands 11 1.426830722 Zagros Mountains forest steppe 17 1.735852987 Registan-North Pakistan sandy desert 0 0 Baluchistan xeric woodlands 0 0 Central Persian desert basins 16 2.169037728 Elburz Range forest steppe 16 1.536816092 Badghyz and Karabil semi-desert 0 0 Kopet Dag woodlands and forest steppe 6 1.494864328 Kopet Dag semi-desert 1 0 Caspian lowland desert 3 1.054920168 Arabian Desert and East Sahero-Arabian xeric shrublands 0 0 Tigris-Euphrates alluvial salt marsh 0 0 Middle East steppe 0 0 Mesopotamian shrub desert 0 0 Caspian Hyrcanian mixed forests 19 2.214329172 Eastern Anatolian montane steppe 10 2.011109471 Azerbaijan shrub desert and steppe 0 0 The biodiversity metrics and statistical trends across Iran's ecoregions underscore the significant variation in Lepidoptera diversity, shaped by ecological characteristics and habitat suitability. Table (2) highlights the stark contrast between forested regions, which provided more favorable habitats, and arid regions, which posed challenges for sustaining Lepidoptera diversity. Factors such as vegetation cover, climate, and resource availability likely influenced these patterns. These findings are consistent with global trends, where forested and semi-arid regions exhibit higher biodiversity metrics due to their capacity to sustain complex trophic interactions and serve as refugia during climatic shifts (Harrison and Noss 2017; Hill et al. 2021). Generalized Linear Model Analysis The Negative Binomial Model Summary is shown in Table (3). Significant positive associations between biodiversity and regions such as the Caspian Hyrcanian mixed forests, Zagros Mountains forest steppe, and Elburz Range forest steppe were highlighted by the model. In contrast, non-significant relationships with biodiversity were shown for regions like the Kopet Dag woodlands and forest steppe, as well as the Caspian lowland desert. Additionally, it was revealed that temperature had a positive effect, while precipitation negatively influenced biodiversity, with sampling effort also being identified as a significant factor. A Negative Binomial GLM was employed to analyze the effects of ecoregions, temperature, precipitation, and sampling intensity on family richness (Table 4). Scaled temperature positively correlated with family richness, indicating that higher temperatures may enhance Lepidoptera diversity by extending growing seasons or improving resource availability. Conversely, precipitation showed a negative relationship, reflecting potential adverse effects of excessive rainfall on habitat stability. Sampling intensity exhibited the strongest positive association with family richness, emphasizing the critical role of data collection in biodiversity assessments. Ecoregion-specific effects revealed significant spatial variation in Lepidoptera family richness. Regions such as the Caspian Hyrcanian mixed forests and Central Persian desert basins exhibited the most substantial positive effects, underscoring their importance as biodiversity hotspots. In contrast, arid ecoregions like the Kopet Dag semi-desert showed negligible or negative effects, reflecting constrained biodiversity due to harsh environmental conditions. Table 3. Negative binomial model summary results Variables coef std err z P>|z| [0.025 0.975] Intercept 0.2229 0.283 0.788 0 0.431 -0.332 0.777 Caspian Hyrcanian mixed forests 1.2827 0.284 4.522 0.000 0.727 1.839 Zagros Mountains forest steppe 0.9602 0.283 3.391 0.001 0.405 1.515 Elburz Range forest steppe 1.2337 0.283 4.353 0.000 0.678 1.789 Eastern Anatolian montane steppe 0.6381 0.287 2.226 0.026 0.076 1.200 Kopet Dag woodlands and forest steppe 0.2218 0.313 0.708 0.479 -0.392 0.836 Kuh Rud and Eastern Iran montane woodlands 0.5308 0.284 1.870 0.061 -0.026 1.087 Caspian lowland desert 0.3303 0.956 0.346 0.730 -1.543 2.203 Central Persian desert basins 1.1623 0.283 4.107 0.000 0.608 1.717 Kopet Dag semi-desert -0.0131 0.762 -0.017 0.986 -1.506 1.480 South Iran Nubo-Sindian desert and semi-desert 0.5192 0.285 1.823 0.068 -0.039 1.077 scaled_temp 0.0902 0.009 10.591 0.000 0.074 0.107 scaled_precip -0.0292 0.007 -4.172 0.000 -0.043 -0.015 scaled_sampling 0.3319 0.005 64.135 0.000 0.322 0.342 Table 4. Generalized linear model regression results Dep. Variable FamilyRichness No. Observations 42603 Model GLM Df Residuals 42589 Model Family NegativeBinomial Df Model 13 Link Function Log Scale 1.0000 Method IRLS Log-Likelihood -1.0070e+05 No. Iterations 22 Deviance 10868 Covariance Type nonrobust Pearson chi2 1.11e+04 Pseudo R-squ. (CS) 0.09697 The results of the Negative Binomial GLM reveal critical insights into the drivers of Lepidoptera family richness across Iran's diverse ecoregions. Significant positive effects observed for the Caspian Hyrcanian mixed forests, Zagros Mountains forest steppe, and Elburz Range forest steppe reinforce their established roles as biodiversity hotspots, likely attributable to their favorable climatic conditions, ecological heterogeneity, and resource availability (Rahbek et al., 2019; Antonelli et al., 2017). These ecoregions, characterized by stable temperatures, varied vegetation structures, and reduced environmental extremes, provide optimal habitats for Lepidoptera, which are highly sensitive to temperature and moisture variations (Parmesan 2006; Sinclair et al. 2012). The positive association between temperature and family richness supports findings that rising temperatures can extend growing seasons, enhance plant productivity, and improve resource availability for Lepidoptera, leading to increased richness in thermally favorable regions (Crozier 2003; Buckley et al. 2010). This trend aligns with global observations of range redistributions and population expansions in butterfly and moth species driven by warming climates (Hickling et al. 2006; Wilson and Maclean 2011). However, the observed negative effect of precipitation may indicate that excessive rainfall destabilizes habitats, reduces foraging efficiency, or disrupts synchrony with host plants, thereby limiting Lepidoptera diversity (Watt and Woiwod 1999; Bale et al. 2002). These results highlight the complexity of Lepidoptera responses to climatic variables, which can vary depending on species traits, habitat type, and geographic location (Sinclair et al. 2012; Hill et al. 2021). Sampling effort emerged as the strongest predictor of family richness, underscoring the critical role of data completeness in biodiversity assessments. Ecoregions such as the Central Persian desert basins and Eastern Anatolian montane steppe, which showed significant positive relationships, reflect the impact of targeted sampling on uncovering hidden diversity, particularly in understudied regions (Beck et al. 2013; Montgomery et al. 2021). Conversely, regions like the Kopet Dag semi-desert and Caspian lowland desert, where biodiversity effects were negligible or non-significant, exemplify the challenges posed by harsh environmental conditions and insufficient data coverage (Titley et al. 2017; Rocha-Ortega et al. 2021). The findings emphasize the need for systematic and standardized sampling approaches to reduce data gaps, particularly in arid and semi-arid ecoregions, where Lepidoptera diversity may be underreported. Incorporating advanced methodologies such as species distribution models (SDMs), environmental DNA (eDNA) metabarcoding, and remote sensing can help overcome sampling biases and provide a clearer understanding of Lepidoptera responses to environmental drivers (Elith and Leathwick 2009; Maino et al. 2016; Van Klink et al. 2022). GBIF Data vs. Observational Data The comparison between GBIF data and the detailed observational dataset from the Catalogue of the Lepidoptera of Iran (Rajaei et al. 2023) reveals both complementary strengths and limitations in capturing the true extent of Lepidoptera biodiversity across Iran's diverse ecoregions. GBIF data, spanning 1993–2022, identifies 27 unique families of Lepidoptera, with significant family richness reported in regions like the Caspian Hyrcanian mixed forests (19 families and 3880 occurrences) and Elburz Range forest steppe (16 families). These results are consistent with global patterns of insect diversity, where ecologically favorable regions such as temperate forests and mountainous areas support high species richness due to complex microhabitats and stable climatic conditions (Antonelli et al. 2018; Rahbek et al. 2019). However, GBIF’s broad-scale, generalized view underrepresents localized occurrences and specialized families, particularly in arid regions like the Arabian Desert and East Sahero-Arabian xeric shrublands, where fewer records (and environmental constraints) are observed. The Catalogue of the Lepidoptera of Iran by Rajaei et al. (2023), in contrast, documents 70 unique families with a provincial-level resolution, providing valuable insights into localized distributions and habitat preferences. For instance, families like Meessiidae and Pterophoridae, which show limited presence in GBIF data, are recorded across multiple provinces in the catalogue. These families’ occurrences underscore their specialized adaptations to specific microhabitats, particularly in regions such as the Zagros Mountains forest steppe and Fars province, where 17 families are reported. This fine-scale data complements GBIF findings by highlighting regional biodiversity hotspots and revealing lesser-known families that are critical for understanding ecological processes and prioritizing conservation (Farashi and Shariati 2017; Wagner et al. 2021). Such granular data are essential for identifying habitats with high conservation value, particularly as many specialist Lepidoptera families remain vulnerable to habitat fragmentation and climate variability (Fox 2013; Hill et al. 2021). Together, GBIF and the catalogue datasets serve as valuable tools for assessing Lepidoptera biodiversity, despite their respective limitations. While GBIF provides a broad-scale, long-term perspective essential for identifying overarching trends, its reliance on opportunistic and unevenly distributed records can result in gaps, particularly in under-sampled or extreme environments like deserts (Montgomery et al. 2021; Rocha-Ortega et al. 2021). On the other hand, the catalogue compensates for these shortcomings by offering localized, taxonomically detailed observations, which are critical for understanding species’ habitat specificity and finer-scale patterns. The integration of both datasets offers a more comprehensive assessment of Lepidoptera biodiversity in Iran, emphasizing the need for standardized monitoring efforts that combine broad-scale citizen science platforms like GBIF with targeted provincial surveys. Such combined approaches are vital for addressing sampling biases, capturing both generalist and specialist taxa, and informing conservation strategies to safeguard Iran’s rich yet vulnerable Lepidoptera diversity (Beck et al. 2013; Chowdhury et al. 2022). Ecological Implications and Conservation Priorities The results of this study highlight the critical ecological role of specific Iranian ecoregions, such as the Caspian Hyrcanian mixed forests, Zagros Mountains forest steppe, and Elburz Range forest steppe, as biodiversity hotspots for Lepidoptera. These regions exhibit consistently high family richness, balanced family composition, and gradual cumulative increases in diversity over time, underscoring their importance as refugia for both generalist and specialist species. The mountainous topography and climatic stability of these areas provide favorable conditions for Lepidoptera to persist, adapt, and speciate (Harrison and Noss 2017; Hill et al. 2021). In contrast, arid and semi-arid regions such as the Registan-North Pakistan sandy desert and Baluchistan xeric woodlands face significant ecological challenges due to extreme abiotic conditions and limited habitat resources. These findings align with global patterns, where desert ecosystems often support lower biodiversity due to physiological constraints on ectothermic organisms like Lepidoptera (Bale et al. 2002; Kingsolver et al. 2011). The observed spatial mismatches between conservation priority hotspots and existing PAs (Noori et al. 2024) underline the urgent need to expand and upgrade Iran's network of PAs to safeguard Lepidoptera biodiversity, especially in ecologically significant mountainous and forested regions. Furthermore, the pronounced effect of temperature on Lepidoptera family richness, coupled with the negative influence of excessive precipitation, emphasizes the vulnerability of these insects to climate change-induced habitat alterations. The ecological implications of these results call for immediate and region-specific conservation priorities to address the threats posed by climate change, habitat loss, and uneven sampling efforts. The substantial mismatch between conservation areas and biodiversity-rich regions, as observed in hotspots like the Alborz and Zagros mountain ranges, further exacerbates the risk of species decline (Rajaei et al. 2023; Noori et al. 2024). To mitigate these risks, targeted conservation strategies should prioritize the expansion of PAs and the enhancement of management efforts in biodiversity-rich regions, particularly those hosting endemic and specialist Lepidoptera. Additionally, standardized, long-term monitoring programs integrating broad-scale datasets such as GBIF with localized surveys are essential for filling data gaps and improving our understanding of biodiversity trends (Beck et al. 2013; Montgomery et al. 2021). Incorporating modern tools such as species distribution models (SDMs) and environmental DNA (eDNA) metabarcoding will enable more precise identification of critical habitats, while addressing anthropogenic pressures like overgrazing, habitat fragmentation, and land-use changes (Amiraslani and Dragovich 2011; Jowkar et al. 2016). Collectively, these measures are crucial for preserving Lepidoptera biodiversity in Iran, which serves as an essential indicator of ecosystem health and a vital component of ecological processes. Conclusion The findings of this study highlight the critical need for targeted conservation efforts in biodiversity-rich ecoregions of Iran, particularly forested domains such as the Caspian Hyrcanian mixed forests and Zagros Mountains forest steppe, which exhibit consistently high Lepidoptera family richness. Equally important is addressing the significant sampling gaps in underrepresented arid and semi-arid ecoregions to ensure comprehensive biodiversity assessments. Integrating standardized data collection into global platforms like GBIF, complemented by localized surveys, can provide a more holistic understanding of Lepidoptera distribution and trends. Adaptive management strategies that mitigate the impacts of climate change by addressing temperature and precipitation variations are essential for safeguarding Lepidoptera as vital indicators of ecosystem health. By prioritizing these measures, conservation efforts can help preserve both the biodiversity and ecological resilience of Iran’s unique ecoregions in the face of ongoing environmental changes. References Amiraslani F, Dragovich D (2011) Combating desertification in Iran over the last 50 years: An overview of changing approaches. J Environ Manage 92:1–13. Antonelli A (2017) Biogeography: drivers of bioregionalization. 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Rocha-Ortega M, Rodriguez P, Córdoba-Aguilar A (2021) Geographical, temporal and taxonomic biases in insect GBIF data on biodiversity and extinction. Ecol Entomol 46:718–728. Sinclair BJ, Williams CM, Terblanche JS (2012) Variation in thermal performance among insect populations. Physiol Biochem Zool 85:594–606. Titley MA, Snaddon JL, Turner EC (2017) Scientific research on animal biodiversity is systematically biased towards vertebrates and temperate regions. PLoS One 12:e0189577. Valizadeh R (2010) Iranian sheep and goat industry at a glance. In: Karim SA, Joshi A (eds) Climate change and stress management: Sheep and goat production. Satish Serial Publishing House, New Delhi, pp 547–551. Van Klink R et al. (2022) Emerging technologies revolutionize insect ecology and monitoring. Trends Ecol Evol 37:872–885. Wagner DL, Grames EM, Forister ML, Berenbaum MR, Stopak D (2021) Insect decline in the Anthropocene: death by a thousand cuts. Proc Natl Acad Sci 118: e2023989118. 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Cite Share Download PDF Status: Published Journal Publication published 23 Oct, 2025 Read the published version in Journal of Insect Conservation → Version 1 posted Editorial decision: Revision requested 10 Apr, 2025 Reviews received at journal 31 Mar, 2025 Reviews received at journal 23 Mar, 2025 Reviewers agreed at journal 26 Jan, 2025 Reviewers agreed at journal 24 Jan, 2025 Reviewers invited by journal 24 Jan, 2025 Editor assigned by journal 19 Dec, 2024 Submission checks completed at journal 19 Dec, 2024 First submitted to journal 18 Dec, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5670034","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":392437055,"identity":"7ea0ff1c-7ff5-49ec-b886-335b102d32d6","order_by":0,"name":"Mohammad Shojaaddini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACCQYGAwYGZsY2BsbGBzDBA8RqaTYgWgsDSEsDAwObBFHukp/dfKDgR421bJ90c1t1YVsdA3/7AcbDFXi0GNw5lmDYcyzduE3mYNvtmW2HGSTOJDAcPINPi0SOgQEP2+HENonEttu8bUBf3GBgONiAz2EzcgwM//yDaCnmBTpMnpAWhhs5Bsa8bRAtzLxtzAwGhLQY3EhLMJbtA/pFIrFZmufcYR7DM4kNBByWfMzwzTdr2fkz0h9+5imrk5M7fvjwR7wOA0aHATKPh4GBkYAGYDQ+IKRiFIyCUTAKRjgAAKP3Te6XdA5fAAAAAElFTkSuQmCC","orcid":"","institution":"National University of Skills (NUS)","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"","lastName":"Shojaaddini","suffix":""}],"badges":[],"createdAt":"2024-12-18 13:53:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5670034/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5670034/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10841-025-00723-2","type":"published","date":"2025-10-23T16:17:22+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":72253674,"identity":"2e66c94d-cc6a-43e1-a272-7fbf242a5bb6","added_by":"auto","created_at":"2024-12-24 09:09:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":410331,"visible":true,"origin":"","legend":"\u003cp\u003eIranian Ecoregions Derived from the Global Ecoregions (Olson et al., 2001).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5670034/v1/f31f7e59011547f799f12e87.png"},{"id":72255089,"identity":"d3ed9a17-dbe1-436f-b64b-7ef10d580103","added_by":"auto","created_at":"2024-12-24 09:25:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":502585,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of lepidoptera biodiversity across Iran ecoregions. Family occurrence points were mapped as black dots.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5670034/v1/9620bdcc2f1b9f5ecb0bad99.png"},{"id":72252599,"identity":"34b9489d-6d18-4499-a303-e753f21347f3","added_by":"auto","created_at":"2024-12-24 09:01:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":97690,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal trends in lepidoptera family richness across Iranian ecoregions (1993-2022).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5670034/v1/760bb8257f5b067a81b411a3.png"},{"id":72252595,"identity":"608117ad-8e34-4f16-a316-0a5707acf430","added_by":"auto","created_at":"2024-12-24 09:01:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":127363,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual turnover rates of lepidoptera families in Iran ecoregions (1993-2022)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5670034/v1/ecf9a296925080e526a0dd23.png"},{"id":72253937,"identity":"1304303e-81b2-4097-902c-d294cee0fd2f","added_by":"auto","created_at":"2024-12-24 09:17:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5713,"visible":true,"origin":"","legend":"\u003cp\u003eCumulative family richness trends across Iranian ecoregions (1993–2022)\u003c/p\u003e","description":"","filename":"placeholderimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-5670034/v1/5e12f46a0139cd4d337d51a7.png"},{"id":94490230,"identity":"99af2b4d-c8f6-4b0c-a0b1-39411bb26587","added_by":"auto","created_at":"2025-10-27 17:08:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2095344,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5670034/v1/ed8d6f93-fc14-4ce4-a796-37600bc23dac.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impacts of Climate Change on Lepidoptera Biodiversity in Iran: Insights from Long-Term Climate Data and GBIF Records","fulltext":[{"header":"Implications for insect conservation","content":"\u003cp\u003eConservation efforts in Iran should prioritize biodiversity-rich ecoregions, particularly forested areas, which consistently exhibit high Lepidoptera family richness. Addressing sampling gaps in underrepresented ecoregions, such as arid and semi-arid regions, is equally important for enhancing biodiversity assessments and developing robust conservation strategies. Standardized data collection, alongside the integration of biodiversity records into platforms with advanced temporal and geospatial monitoring capabilities, such as GBIF, is crucial for capturing trends in both generalist and specialist Lepidoptera families. In addition, adaptive management strategies should prioritize mitigating climate change impacts of temperature and precipitation. Such measures will help preserve Lepidoptera, which are essential indicators of ecosystem health and climate resilience in Iran.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eBiodiversity in Iran faces significant challenges due to both anthropogenic pressures and climatic changes, threatening various ecosystems and species across the country. Notably, modern infrastructure development, including extensive road systems, has fragmented natural habitats, resulting in wildlife-vehicle collisions that further disrupt biodiversity (Jowkar et al. 2016). In addition, unsustainable practices such as over-grazing and over-cutting have led to soil degradation, transforming once productive rangelands into desert-like conditions, thereby jeopardizing the flora and fauna dependent on these ecosystems (Valizadeh 2010; Amiraslani and Dragovich 2011; Jowkar et al. 2016). The rapid increase in Iran's human population, which tripled from approximately 25 million in 1965 to 76–78 million by 2013, has compounded these issues by intensifying demand for land, water, and resources, further exacerbating the loss of biodiversity (Jowkar et al. 2016).\u003c/p\u003e\n\u003cp\u003eConserving biodiversity requires a comprehensive understanding of insect distributions and the identification of critical habitats and biodiversity hotspots (Myers et al. 2000; Dobson et al. 2006). In the case of Iran, despite its recognition as a global biodiversity hotspot, the family richness has not been thoroughly studied, particularly for groups such as Lepidoptera (Mittermeier et al. 2004). Iran’s Lepidoptera fauna is particularly significant, with at least 4,812 species recorded, 19.7% of which are endemic (Rajaei et al. 2023). However, the protection of these species faces considerable challenges, as the country's protected areas (PAs) often do not coincide with biodiversity hotspots, leaving critical areas, such as the Alborz and Zagros Mountains, under-protected (Rajaei et al. 2023; Noori et al. 2024). This mismatch between biodiversity-rich regions and PAs underscores the need for better management strategies to conserve species, particularly endemic and threatened Lepidoptera (Farashi and Shariati, 2017; Noori et al. 2021).\u003c/p\u003e\n\u003cp\u003eInsects, particularly Lepidoptera, are highly sensitive to climate change, with temperature and precipitation acting as primary drivers of their distribution, phenology, and population dynamics (Crozier 2003; Hill et al. 2021). Climate change has already been observed to affect the abundance and geographical ranges of many Lepidoptera, with warmer conditions and altered precipitation patterns often leading to population declines and range contractions (Woiwod 1997; Parmesan 2006). The sensitivity of butterflies and moths to these changes makes them excellent indicators of broader ecological shifts, as they are able to reflect both direct and indirect effects of climate change, including alterations in species interactions and habitat availability (Warren et al. 2001; Wilson and Maclean 2011).\u003c/p\u003e\n\u003cp\u003eWhile the general understanding of climate change impacts on Lepidoptera is growing, gaps remain in our knowledge of how these changes unfold across different regions. Studies using long-term data, such as the Butterfly Monitoring Scheme (BMS) in Europe, have provided valuable insights into how Lepidoptera populations are responding to climate variability (Brereton et al. 2006). However, in regions like Iran, where comprehensive biodiversity monitoring is still developing, such data are scarce, and the existing biodiversity records are often biased by uneven geographic and taxonomic coverage (Beck et al. 2014; Rocha-Ortega et al. 2021). The Global Biodiversity Information Facility (GBIF) has become a crucial resource in addressing these data gaps by providing extensive species occurrence data, though it too faces challenges in data representation, particularly for invertebrate taxa like Lepidoptera (Beck et al. 2013; Codata et al. 2020).\u003c/p\u003e\n\u003cp\u003eThis study seeks to address these challenges by leveraging the extensive and increasingly accessible GBIF dataset to analyze Lepidoptera biodiversity in Iran from 1993 to 2023. By integrating this data with long-term climate records from the ERA5-Land dataset, it was hypothesized that climate change has driven significant shifts in the distribution and diversity of Lepidoptera families across Iran’s ecoregions. In addition to examining the temporal and spatial dynamics of Lepidoptera biodiversity, this study also evaluates the utility of GBIF data as a tool for biodiversity monitoring. A comparison between GBIF occurrence data (1993–2022) and the most recent Catalogue of the Lepidoptera of Iran (Rajaei et al. 2023)—the most comprehensive and up-to-date reference on the country's Lepidoptera—was conducted. By cross-referencing family-level data from both sources, this study identifies discrepancies and gaps in biodiversity coverage. Furthermore, spatial overlap between GBIF occurrence records and national ecoregions was assessed through geospatial analysis, providing insights into how well GBIF data represents the ecological diversity across Iranian provinces. This evaluation of data quality and spatial representation contributes to a better understanding of the broader implications of using GBIF data for long-term biodiversity monitoring.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Area, Data Collection and Temporal Scope\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study covered the entire area of Iran, which spans about 1,650,000 km\u0026sup2; and includes all 31 provinces. The study boundaries were defined using Iran Shapefile data from the National Geographical Organization of Iran. A geo-referenced dataset of Lepidoptera records in Iran was retrieved from the Global Biodiversity Information Facility (GBIF) on 9 November 2024. This dataset included 3957 records from 1993 to 2022, compiled from seven datasets provided by publishers from seven different countries (GBIF.org, 2024). The analysis focused on biodiversity trends at the family level. To ensure accuracy, spatial mapping was done using Python 3.11 within Iran Ecoregions specifically designed for Iran, aligning with its geographical and ecological features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIran Ecoregions Shapefile Creation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Iran Ecoregions Shapefile was crafted using Python 3.11 libraries. Initially, the Iran shapefile (National Geographical Organization of Iran) and the global ecoregions shapefile sourced from the Terrestrial Ecoregions of the World dataset (Olson et al. 2001) were read using Geopandas. Subsequently, the ecoregions GeoDataFrame was reprojected to align with the Coordinate Reference System (CRS) of the Iran GeoDataFrame. Geopandas was then employed to conduct a spatial join between the Iran shapefile and ecoregions shapefile, integrating geographical attributes. The ecoregions were clipped to Iran\u0026apos;s boundary, refining the dataset\u0026apos;s spatial extent. The resulting Iran Ecoregions Shapefile (Fig. 1) was utilized for diversity analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLong-Term Climate Data Collection and Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTemperature and precipitation data were obtained from the ERA5-Land reanalysis dataset, provided by the Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (Mu\u0026ntilde;oz Sabater 2019). Data spanning the years 1993 to 2022 were downloaded programmatically using Python 3.11 and the cdsapi library, which facilitated automated requests to the CDS API. The geographic focus was limited to the extent of Iran, defined by latitude (25\u0026deg;N\u0026ndash;40\u0026deg;N) and longitude (44\u0026deg;E\u0026ndash;63\u0026deg;E). Both datasets were retrieved in NetCDF format. For temperature, the monthly averaged 2-meter air temperature variable was converted from Kelvin to Celsius. Monthly data were then averaged to produce yearly mean temperature grids. For precipitation, the total precipitation variable was converted from meters (as provided by ERA5) to millimeters. Monthly totals were summed to calculate yearly precipitation grids. All calculations were performed using the Python 3.11 xarray library. The resulting yearly datasets for temperature and precipitation were aggregated into separate datasets spanning all years. These datasets retained spatial dimensions (latitude and longitude) and incorporated a temporal dimension (year).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial Biodiversity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate Lepidoptera biodiversity across Iranian ecoregions, Python 3.11 was utilized along with the libraries including pandas, geopandas, matplotlib, shapely, and numpy for spatial data manipulation, analysis, and visualization. GBIF occurrence data for Lepidoptera families, containing complete latitude, longitude, and family classification information, was loaded as a GeoDataFrame in the WGS84 CRS and overlaid with Iranian ecoregion boundaries using shapefiles in geopandas. The ecoregion boundaries were clipped to match Iran\u0026rsquo;s outline, providing a precise national boundary. A spatial join was conducted to associate each occurrence point with an ecoregion, allowing family richness (the count of unique families) to be calculated for each ecoregion. Family richness values were merged with ecoregion polygons, and heatmaps were generated to illustrate biodiversity distribution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiodiversity Metrics Calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate Lepidoptera biodiversity across Iran\u0026apos;s terrestrial ecoregions, Family Richness, the Shannon-Wiener Index, and descriptive statistics were calculated using occurrence data from the GBIF. Initially, ecoregion shapefiles and Lepidoptera occurrence records, which included family-level taxonomy and geographic coordinates, were imported into a Geographic Information System. A spatial join was conducted to link each occurrence record to its respective ecoregion, excluding \u0026quot;Lake\u0026quot; ecoregions to focus exclusively on terrestrial habitats. For each terrestrial ecoregion, Family Richness was calculated as the number of unique Lepidoptera families observed. The Shannon-Wiener Index was then calculated to assess biodiversity, taking into account both family richness and evenness within each ecoregion. This index was computed using the formula:\u003c/p\u003e\n\u003cp\u003eH\u0026prime;= -\u0026sum;(p\u003csub\u003ei\u003c/sub\u003e\u0026sdot;ln(p\u003csub\u003ei\u003c/sub\u003e))\u003c/p\u003e\n\u003cp\u003ewhere:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eH\u0026prime; represents the Shannon-Wiener Index, an indicator of biodiversity,\u003c/li\u003e\n \u003cli\u003ep\u003csub\u003ei\u003c/sub\u003e is the relative frequency (proportion) of occurrences of each family within the ecoregion, calculated as the number of occurrences of family divided by the total occurrences across all families in that ecoregion.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003eFinally, descriptive statistics, including mean, standard deviation, and range, were computed for both metrics to summarize patterns across ecoregions. The biodiversity metrics and descriptive statistics were saved to an Excel file for subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal Trends in Family Richness and Turnover Rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess long-term biodiversity trends in Lepidoptera across Iran\u0026apos;s ecoregions, family richness and turnover rate metrics were calculated using data from GBIF occurrence records. Family richness, defined as the count of unique families observed per year within each ecoregion, was derived by grouping records by year and ecoregion and counting unique family entries, following established biodiversity assessment methods. Turnover rate, indicating changes in family composition over consecutive years, was calculated by comparing the sets of families observed in two consecutive years for each ecoregion. This rate was computed as the ratio of the number of families that appeared or disappeared between years to the total families observed across both years, highlighting annual shifts in family presence. Non-terrestrial ecoregions, such as \u0026quot;Lake,\u0026quot; were excluded to focus on terrestrial biodiversity patterns. Temporal trends were visualized using Seaborn\u0026rsquo;s FacetGrid, which enabled the generation of line plots for family richness and turnover rate over time across ecoregions with observed Lepidoptera. Ecoregions with zero values in richness or turnover were excluded to maintain clarity. All plot elements, including titles and labels, were standardized in Times New Roman font for readability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFamily Accumulative Curves\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFamily accumulative curves were constructed to analyze long-term trends in Lepidoptera biodiversity across Iran\u0026rsquo;s ecoregions. Occurrence data spanning 1993\u0026ndash;2022 was spatially assigned to ecoregions using the Iran Ecoregions shapefile, derived from the Terrestrial Ecoregions dataset (Olson et al. 2001). For each ecoregion, the cumulative number of unique families was calculated annually, with previously recorded families retained and newly observed families incrementally added each year. Ecoregion-specific accumulation curves were plotted using Python 3.11 utilizing distinct colors to distinguish patterns visually.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Modeling of Biodiversity and Climate Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the long-term biodiversity of Lepidoptera across Iran\u0026rsquo;s ecoregions in relation to climate factors, a Generalized Linear Model (GLM) with a Negative Binomial link function was applied. Occurrence data were obtained from GBIF, encompassing records spanning three decades with geographic coordinates (latitude and longitude). Data processing and analysis were performed using Python 3.11, with libraries including pandas for tabular data manipulation, geopandas for spatial operations, statsmodels for statistical modeling, and numpy for numerical computations. Occurrences were spatially integrated with ecoregion shapefiles to assign records to specific ecoregions. Family richness, representing the count of unique Lepidoptera families per ecoregion-year, was selected as the response variable. Predictor variables included mean annual temperature, total annual precipitation, and sampling intensity. Climate variables were derived from georeferenced NetCDF files, averaged across ecoregions using xarray with spatial masking. All predictors were standardized using the StandardScaler function from sklearn to facilitate comparability and interpretability. The GLM was implemented using the glm function from statsmodels, with ecoregion included as a fixed effect to account for geographic variability. Model performance was evaluated through parameter significance, likelihood ratios, and residual diagnostics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation of GBIF Data vs. Observational Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comparison between GBIF occurrence data (1993\u0026ndash;2022) and the latest available Catalogue of the Lepidoptera of Iran was conducted to evaluate biodiversity coverage. The Catalogue of the Lepidoptera of Iran (Rajaei et al. 2023), as the first modern and comprehensive resource on Iranian Lepidoptera, provided national coverage for the entire order that synthesized all presently-known phylogenetic, systematic, taxonomic, nomenclatural, and geographic information (province level) on Iranian Lepidoptera (Rajaei and Karsholt 2023). The list of unique Lepidoptera families was extracted from GBIF occurrence data, which spans 1993 to 2022 and includes georeferenced records across various provinces and ecoregions. Each family name was cross-checked between the GBIF dataset and the Catalogue to identify discrepancies and gaps in biodiversity coverage. To evaluate the spatial overlap between ecoregions and provinces in Iran, geospatial analysis was performed using Python 3.11 GeoPandas library. Ecoregion and provincial boundary shapefiles for Iran were loaded, their CRS aligned, and the ecoregions clipped to Iran\u0026apos;s national boundary. Spatial intersections were calculated to determine the percentage of area each province covers within an ecoregion. Provinces encompassing between 20% and 100% of an ecoregion\u0026rsquo;s area were recorded.\u0026nbsp;\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eSpatial Distribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis of Lepidoptera biodiversity across Iran\u0026apos;s terrestrial ecoregions, as represented in Fig. (2), revealed significant variation in family richness and diversity levels among different habitats. Ecoregions such as the Caspian Hyrcanian mixed forests, Central Persian desert basins, and Zagros Mountains forest steppe exhibited the highest family richness, indicating that these areas likely support diverse and favorable environmental conditions for Lepidoptera. In contrast, several ecoregions, including the Registan-North Pakistan sandy desert, Baluchistan xeric woodlands, and Arabian Desert xeric shrublands, showed zero family richness, suggesting limited suitability for Lepidoptera due to extreme or resource-scarce conditions (Fig. 2).\u003c/p\u003e\n\u003cp\u003eThe observed variation in Lepidoptera family richness across Iran\u0026apos;s terrestrial ecoregions highlights both the ecological significance of mountainous habitats and the vulnerability of biodiversity to climatic and anthropogenic pressures. The Caspian Hyrcanian mixed forests, Central Persian desert basins, and Zagros Mountains forest steppe emerged as biodiversity hotspots, aligning with findings by Noori et al. (2024) and Rajaei et al. (2023), which emphasized the role of the Alborz and Zagros mountain ranges as centers for Lepidoptera richness and endemism. These regions, characterized by complex topography and microhabitats, act as refugia, enabling species to persist through historical climatic fluctuations (Harrison and Noss 2017). In contrast, the limited or zero richness in arid ecoregions such as the Registan-North Pakistan sandy desert and Baluchistan xeric woodlands underscores the sensitivity of Lepidoptera to extreme abiotic conditions, consistent with studies showing the dependence of these ectothermic organisms on temperature and moisture availability (Kingsolver et al. 2011; Hill et al. 2021). Human activities, such as overgrazing, habitat fragmentation, and land-use changes, further exacerbate these patterns (Amiraslani and Dragovich 2011; Jowkar et al. 2016). Moreover, climate change-induced shifts in temperature and precipitation could drive range redistributions and local extinctions of vulnerable insects in these already resource-limited regions (Crozier 2003; Lenoir et al. 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFamily-Level Biodiversity Across Ecoregions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable (1) summarizes Lepidoptera family richness and occurrences across the ecoregions of Iran from 1993 to 2022. Ecoregions with higher sampling intensity, such as the Caspian Hyrcanian mixed forests and Zagros Mountains forest steppe, were characterized by greater family richness, with 19 and 17 families recorded, respectively. Conversely, regions like the Registan-North Pakistan sandy desert reported only a few occurrences, reflecting incomplete sampling efforts that may have led to underrepresentation of Lepidoptera diversity. The data underscore the critical role of comprehensive and uniform sampling across all regions of Iran in obtaining accurate biodiversity assessments.\u003c/p\u003e\n\u003cp\u003eEcoregions with more extensive sampling, such as the Elburz Range forest steppe and Central Persian desert basins, exhibited higher family richness. In contrast, sparsely sampled regions, including the Tigris-Euphrates marshes and Mesopotamian shrub desert, were underrepresented, emphasizing the need to address sampling bias. Without such corrections, biodiversity estimates could underestimate richness in less-sampled regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Lepidoptera family richness and occurrence summary of Iran ecoregions (1993-2022)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"595\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcoregion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Number of Occurrences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Number of Unique Families\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eList of Unique Families\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eArabian Desert and East Sahero-Arabian xeric shrublands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eAzerbaijan shrub desert and steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eBadghyz and Karabil semi-desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eBaluchistan xeric woodlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eCaspian Hyrcanian mixed forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eNoctuidae, Geometridae, Nymphalidae, Crambidae, Pieridae, Erebidae, Lycaenidae, Zygaenidae, Cossidae, Sphingidae, Notodontidae, Papilionidae, Hesperiidae, Pyralidae, Sesiidae, Depressariidae, Lasiocampidae, Tineidae, Gelechiidae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eCaspian lowland desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eGeometridae, Lasiocampidae, Lycaenidae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eCentral Persian desert basins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e9586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eGeometridae, Noctuidae, Saturniidae, Nymphalidae, Pieridae, Lycaenidae, Psychidae, Hesperiidae, Papilionidae, Sphingidae, Plutellidae, Erebidae, Pyralidae, Tischeriidae, Crambidae, Zygaenidae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eEastern Anatolian montane steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eGeometridae, Sesiidae, Nymphalidae, Noctuidae, Papilionidae, Crambidae, Pterophoridae, Erebidae, Pieridae, Lycaenidae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eElburz Range forest steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e12919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eGeometridae, Noctuidae, Nymphalidae, Lycaenidae, Hesperiidae, Pieridae, Sphingidae, Papilionidae, Erebidae, Pyralidae, Tineidae, Sesiidae, Zygaenidae, Crambidae, Drepanidae, Saturniidae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eKopet Dag semi-desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003ePapilionidae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eKopet Dag woodlands and forest steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eGeometridae, Nymphalidae, Sphingidae, Lycaenidae, Erebidae, Pieridae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eKuh Rud and Eastern Iran montane woodlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eGeometridae, Noctuidae, Nymphalidae, Pieridae, Lycaenidae, Erebidae, Notodontidae, Hesperiidae, Sphingidae, Sesiidae, Zygaenidae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eMesopotamian shrub desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eMiddle East steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eRegistan-North Pakistan sandy desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eSouth Iran Nubo-Sindian desert and semi-desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003ePieridae, Nymphalidae, Geometridae, Lycaenidae, Erebidae, Noctuidae, Notodontidae, Sphingidae, Pterophoridae, Hesperiidae, Papilionidae, Plutellidae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eTigris-Euphrates alluvial salt marsh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003eZagros Mountains forest steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003e14301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 313px;\"\u003e\n \u003cp\u003eGeometridae, Crambidae, Nolidae, Nymphalidae, Papilionidae, Lycaenidae, Cossidae, Erebidae, Sesiidae, Pieridae, Hesperiidae, Sphingidae, Noctuidae, Pyralidae, Tineidae, Meessiidae, Zygaenidae\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe family-level biodiversity results across Iran\u0026apos;s ecoregions emphasize the influence of both ecological factors and sampling bias on Lepidoptera diversity assessments. Regions such as the Caspian Hyrcanian mixed forests and Zagros Mountains forest steppe, which reported the highest family richness (19 and 17 families, respectively), align with prior studies highlighting these mountainous areas as biodiversity hotspots for Lepidoptera due to their ecological complexity, microhabitat heterogeneity, and historical role as refugia (Harrison and Noss 2017; Rajaei et al. 2023; Noori et al. 2024). These findings are consistent with broader global patterns, where mountainous and forested landscapes support higher insect diversity by acting as barriers and corridors for gene flow, facilitating both dispersal and isolation (Antonelli 2017; Rahbek et al. 2019). Conversely, ecoregions with minimal occurrences, such as the Registan-North Pakistan sandy desert and Baluchistan xeric woodlands, reflect not only extreme environmental conditions but also incomplete and geographically uneven sampling efforts, a challenge well-documented in biodiversity studies relying on opportunistic data (Fattorini 2013; Rocha-Ortega et al. 2021). This sampling bias can underestimate richness and limit our ability to identify critical conservation areas, particularly in regions where Lepidoptera populations remain underexplored. Addressing these biases through standardized monitoring, such as leveraging tools like the GBIF and novel methodologies like environmental DNA (eDNA) metabarcoding, is essential for filling data gaps and improving biodiversity estimates (Beck et al. 2013; Montgomery et al. 2021). Ultimately, the observed disparity in family richness underscores the urgent need for targeted surveys and conservation strategies in underrepresented regions to ensure comprehensive biodiversity management and protection of Lepidoptera diversity in Iran.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemporal Trends in Family Richness and Turnover Rates\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTemporal analyses revealed distinct trends in family richness across the study period (1993\u0026ndash;2022). The Caspian Hyrcanian mixed forests and Zagros Mountains forest steppe consistently exhibited higher family richness, with notable peaks of 10 unique families in 2016 and 8 families in 2021, respectively (Fig. 3). Other regions, like the Central Persian desert basins and Kopet Dag woodlands, showed fluctuating yet generally lower richness levels. Periods with zero recorded family richness in certain ecoregions reflect either a lack of observations or incomplete data.\u003c/p\u003e\n\u003cp\u003eAnnual turnover rates further elucidated dynamic shifts in family composition across ecoregions (Fig. 4). Ecoregions such as the Elburz Range forest steppe and South Iran Nubo-Sindian desert exhibited high turnover rates in specific years, indicating substantial changes in family composition potentially driven by environmental variability or sampling inconsistencies. Conversely, ecoregions like Kuh Rud and Eastern Iran montane woodlands maintained relatively stable family compositions over time, as reflected by turnover rates close to 0.0.\u003c/p\u003e\n\u003cp\u003eThe temporal trends in Lepidoptera family richness and turnover rates across Iranian ecoregions reflect both ecological dynamics and the limitations of available data. The consistently high family richness observed in the Caspian Hyrcanian mixed forests and Zagros Mountains forest steppe aligns with their recognized role as biodiversity hotspots, characterized by favorable environmental conditions, microhabitat heterogeneity, and historical stability that support species persistence and diversity (Rajaei et al. 2023; Noori et al. 2024). Peaks in family richness, such as those recorded in 2016 and 2021, may correspond to years with improved sampling efforts or favorable climatic conditions that positively influenced Lepidoptera populations, as these ectothermic organisms are highly sensitive to temperature and moisture variability (Bale et al. 2002; Kingsolver et al. 2011). In contrast, regions like the Central Persian desert basins and Kopet Dag woodlands exhibited lower and fluctuating richness, consistent with findings that arid and semi-arid habitats often limit insect diversity due to extreme abiotic factors and resource scarcity (Woiwod 1997; Amiraslani and Dragovich 2011).\u003c/p\u003e\n\u003cp\u003eThe observed high turnover rates in ecoregions such as the Elburz Range forest steppe and South Iran Nubo-Sindian desert highlight significant changes in family composition, potentially driven by climatic fluctuations, habitat alterations, or sampling inconsistencies over time. These findings underscore the vulnerability of Lepidoptera to environmental variability, as changes in temperature, rainfall patterns, and habitat availability can directly influence family distributions, population dynamics, and interspecific interactions (Crozier 2003; Lenoir et al. 2020; Hill et al. 2021). Conversely, stable turnover rates in regions like the Kuh Rud and Eastern Iran montane woodlands suggest a degree of ecological resilience, where family composition remains relatively constant despite temporal variability. However, periods with zero recorded family richness in some ecoregions highlight the persistent challenges of incomplete or geographically biased sampling, which can obscure true biodiversity patterns and trends (Fattorini 2013; Rocha-Ortega et al. 2021). Addressing these data gaps through long-term, standardized monitoring programs and leveraging advanced biodiversity tools, such as GBIF and remote sensing technologies, is essential for accurately assessing Lepidoptera richness, turnover dynamics, and their responses to environmental change.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCumulative Family Richness Trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCumulative trends over the 30-year study period revealed significant variation in family richness and sampling completeness across ecoregions (Fig. 5). The Caspian Hyrcanian mixed forests exhibited the highest cumulative family richness, reaching 19 families by 2022, with consistent additions over time. Ecoregions like the Zagros Mountains forest steppe and Central Persian desert basins also demonstrated gradual increases, reaching 17 families each. In contrast, arid regions such as the Arabian Desert and Registan-North Pakistan sandy desert showed no observed family richness, likely due to either limited Lepidoptera presence or insufficient sampling efforts.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSampling completeness varied markedly among ecoregions. The Caspian Hyrcanian mixed forests and Elburz Range forest steppe displayed relatively complete accumulation curves, while arid and semi-arid regions like the South Iran Nubo-Sindian desert showed uneven sampling efforts. Many desert and semi-desert ecoregions demonstrated no observed family richness, emphasizing the uneven distribution of sampling efforts across Iran.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiodiversity Metrics and Statistical Trends\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe statistical summary of biodiversity data revealed that for the ecoregions analyzed, the mean family richness was 5.89 with a standard deviation of 7.13, ranging from 0 to 19. The Shannon-Wiener Index, which measured biodiversity, had a mean of 0.83 and a standard deviation of 0.93, with values ranging from 0 to 2.21. Descriptive statistics for biodiversity metrics provide further insights into overall trends (Table 2). The mean family richness across all ecoregions was relatively low, reflecting limited Lepidoptera diversity in many regions. The Shannon-Wiener Index values were aligned with family richness trends, with the highest values observed in the Caspian Hyrcanian mixed forests and Central Persian desert basins. These regions not only supported high family richness but also exhibited balanced family compositions. The standard deviation for both metrics was high, indicating substantial variability in biodiversity across ecoregions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e Biodiversity metrics of lepidoptera families across Iran ecoregions\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"613\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEcoregion\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFamily Richness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShannon Wiener Index\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eSouth Iran Nubo-Sindian desert and semi-desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e2.031745536\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eKuh Rud and Eastern Iran montane woodlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e1.426830722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eZagros Mountains forest steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e1.735852987\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eRegistan-North Pakistan sandy desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eBaluchistan xeric woodlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eCentral Persian desert basins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e2.169037728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eElburz Range forest steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e1.536816092\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eBadghyz and Karabil semi-desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eKopet Dag woodlands and forest steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e1.494864328\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eKopet Dag semi-desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eCaspian lowland desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e1.054920168\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eArabian Desert and East Sahero-Arabian xeric shrublands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eTigris-Euphrates alluvial salt marsh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eMiddle East steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eMesopotamian shrub desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eCaspian Hyrcanian mixed forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e2.214329172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eEastern Anatolian montane steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e2.011109471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 312px;\"\u003e\n \u003cp\u003eAzerbaijan shrub desert and steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 178px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe biodiversity metrics and statistical trends across Iran\u0026apos;s ecoregions underscore the significant variation in Lepidoptera diversity, shaped by ecological characteristics and habitat suitability. Table (2) highlights the stark contrast between forested regions, which provided more favorable habitats, and arid regions, which posed challenges for sustaining Lepidoptera diversity. Factors such as vegetation cover, climate, and resource availability likely influenced these patterns. These findings are consistent with global trends, where forested and semi-arid regions exhibit higher biodiversity metrics due to their capacity to sustain complex trophic interactions and serve as refugia during climatic shifts (Harrison and Noss 2017; Hill et al. 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGeneralized Linear Model Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Negative Binomial Model Summary is shown in Table (3). Significant positive associations between biodiversity and regions such as the Caspian Hyrcanian mixed forests, Zagros Mountains forest steppe, and Elburz Range forest steppe were highlighted by the model. In contrast, non-significant relationships with biodiversity were shown for regions like the Kopet Dag woodlands and forest steppe, as well as the Caspian lowland desert. Additionally, it was revealed that temperature had a positive effect, while precipitation negatively influenced biodiversity, with sampling effort also being identified as a significant factor.\u003c/p\u003e\n\u003cp\u003eA Negative Binomial GLM was employed to analyze the effects of ecoregions, temperature, precipitation, and sampling intensity on family richness (Table 4). Scaled temperature positively correlated with family richness, indicating that higher temperatures may enhance Lepidoptera diversity by extending growing seasons or improving resource availability. Conversely, precipitation showed a negative relationship, reflecting potential adverse effects of excessive rainfall on habitat stability. Sampling intensity exhibited the strongest positive association with family richness, emphasizing the critical role of data collection in biodiversity assessments.\u003c/p\u003e\n\u003cp\u003eEcoregion-specific effects revealed significant spatial variation in Lepidoptera family richness. Regions such as the Caspian Hyrcanian mixed forests and Central Persian desert basins exhibited the most substantial positive effects, underscoring their importance as biodiversity hotspots. In contrast, arid ecoregions like the Kopet Dag semi-desert showed negligible or negative effects, reflecting constrained biodiversity due to harsh environmental conditions.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eNegative binomial model summary results\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ecoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003estd err\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ez\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u0026gt;|z|\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e[0.025\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.975]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.2229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.788\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0 0.431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.777\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eCaspian Hyrcanian mixed forests\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.2827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eZagros Mountains forest steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.9602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e3.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eElburz Range forest steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.2337\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.789\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eEastern Anatolian montane steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.6381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e2.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eKopet Dag woodlands and forest steppe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.2218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eKuh Rud and Eastern Iran montane woodlands\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.5308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eCaspian lowland desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.3303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e0.346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e2.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eCentral Persian desert basins\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e1.1623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e4.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.608\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eKopet Dag semi-desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.0131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-1.506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003eSouth Iran Nubo-Sindian desert and semi-desert\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.5192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e1.823\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e1.077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003escaled_temp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.0902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e10.591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003escaled_precip\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e-0.0292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e-4.172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 276px;\"\u003e\n \u003cp\u003escaled_sampling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e0.3319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e64.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.342\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u0026nbsp;\u003c/strong\u003eGeneralized linear model regression results\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eDep. Variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eFamilyRichness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eNo. Observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e42603\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eGLM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eDf Residuals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e42589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eModel Family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eNegativeBinomial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eDf Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eLink Function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eLog\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eScale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003eIRLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eLog-Likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e-1.0070e+05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eNo. Iterations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003eDeviance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e10868\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003eCovariance Type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003enonrobust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003ePearson chi2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e1.11e+04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 123px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 151px;\"\u003e\n \u003cp\u003ePseudo R-squ. (CS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 104px;\"\u003e\n \u003cp\u003e0.09697\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe results of the Negative Binomial GLM reveal critical insights into the drivers of Lepidoptera family richness across Iran\u0026apos;s diverse ecoregions. Significant positive effects observed for the Caspian Hyrcanian mixed forests, Zagros Mountains forest steppe, and Elburz Range forest steppe reinforce their established roles as biodiversity hotspots, likely attributable to their favorable climatic conditions, ecological heterogeneity, and resource availability (Rahbek et al., 2019; Antonelli et al., 2017). These ecoregions, characterized by stable temperatures, varied vegetation structures, and reduced environmental extremes, provide optimal habitats for Lepidoptera, which are highly sensitive to temperature and moisture variations (Parmesan 2006; Sinclair et al. 2012).\u003c/p\u003e\n\u003cp\u003eThe positive association between temperature and family richness supports findings that rising temperatures can extend growing seasons, enhance plant productivity, and improve resource availability for Lepidoptera, leading to increased richness in thermally favorable regions (Crozier 2003; Buckley et al. 2010). This trend aligns with global observations of range redistributions and population expansions in butterfly and moth species driven by warming climates (Hickling et al. 2006; Wilson and Maclean 2011). However, the observed negative effect of precipitation may indicate that excessive rainfall destabilizes habitats, reduces foraging efficiency, or disrupts synchrony with host plants, thereby limiting Lepidoptera diversity (Watt and Woiwod 1999; Bale et al. 2002). These results highlight the complexity of Lepidoptera responses to climatic variables, which can vary depending on species traits, habitat type, and geographic location (Sinclair et al. 2012; Hill et al. 2021).\u003c/p\u003e\n\u003cp\u003eSampling effort emerged as the strongest predictor of family richness, underscoring the critical role of data completeness in biodiversity assessments. Ecoregions such as the Central Persian desert basins and Eastern Anatolian montane steppe, which showed significant positive relationships, reflect the impact of targeted sampling on uncovering hidden diversity, particularly in understudied regions (Beck et al. 2013; Montgomery et al. 2021). Conversely, regions like the Kopet Dag semi-desert and Caspian lowland desert, where biodiversity effects were negligible or non-significant, exemplify the challenges posed by harsh environmental conditions and insufficient data coverage (Titley et al. 2017; Rocha-Ortega et al. 2021).\u003c/p\u003e\n\u003cp\u003eThe findings emphasize the need for systematic and standardized sampling approaches to reduce data gaps, particularly in arid and semi-arid ecoregions, where Lepidoptera diversity may be underreported. Incorporating advanced methodologies such as species distribution models (SDMs), environmental DNA (eDNA) metabarcoding, and remote sensing can help overcome sampling biases and provide a clearer understanding of Lepidoptera responses to environmental drivers (Elith and Leathwick 2009; Maino et al. 2016; Van Klink et al. 2022).\u0026nbsp;\u003cbr\u003e\u0026nbsp;\u003cbr\u003e\u003cstrong\u003eGBIF Data vs. Observational Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe comparison between GBIF data and the detailed observational dataset from the \u003cem\u003eCatalogue of the Lepidoptera of Iran\u003c/em\u003e (Rajaei et al. 2023) reveals both complementary strengths and limitations in capturing the true extent of Lepidoptera biodiversity across Iran\u0026apos;s diverse ecoregions. GBIF data, spanning 1993\u0026ndash;2022, identifies 27 unique families of Lepidoptera, with significant family richness reported in regions like the Caspian Hyrcanian mixed forests (19 families and 3880 occurrences) and Elburz Range forest steppe (16 families). These results are consistent with global patterns of insect diversity, where ecologically favorable regions such as temperate forests and mountainous areas support high species richness due to complex microhabitats and stable climatic conditions (Antonelli et al. 2018; Rahbek et al. 2019). However, GBIF\u0026rsquo;s broad-scale, generalized view underrepresents localized occurrences and specialized families, particularly in arid regions like the Arabian Desert and East Sahero-Arabian xeric shrublands, where fewer records (and environmental constraints) are observed.\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eCatalogue of the Lepidoptera of Iran\u003c/em\u003e by Rajaei et al. (2023), in contrast, documents 70 unique families with a provincial-level resolution, providing valuable insights into localized distributions and habitat preferences. For instance, families like Meessiidae and Pterophoridae, which show limited presence in GBIF data, are recorded across multiple provinces in the catalogue. These families\u0026rsquo; occurrences underscore their specialized adaptations to specific microhabitats, particularly in regions such as the Zagros Mountains forest steppe and Fars province, where 17 families are reported. This fine-scale data complements GBIF findings by highlighting regional biodiversity hotspots and revealing lesser-known families that are critical for understanding ecological processes and prioritizing conservation (Farashi and Shariati 2017; Wagner et al. 2021). Such granular data are essential for identifying habitats with high conservation value, particularly as many specialist Lepidoptera families remain vulnerable to habitat fragmentation and climate variability (Fox 2013; Hill et al. 2021).\u003c/p\u003e\n\u003cp\u003eTogether, GBIF and the catalogue datasets serve as valuable tools for assessing Lepidoptera biodiversity, despite their respective limitations. While GBIF provides a broad-scale, long-term perspective essential for identifying overarching trends, its reliance on opportunistic and unevenly distributed records can result in gaps, particularly in under-sampled or extreme environments like deserts (Montgomery et al. 2021; Rocha-Ortega et al. 2021). On the other hand, the catalogue compensates for these shortcomings by offering localized, taxonomically detailed observations, which are critical for understanding species\u0026rsquo; habitat specificity and finer-scale patterns. The integration of both datasets offers a more comprehensive assessment of Lepidoptera biodiversity in Iran, emphasizing the need for standardized monitoring efforts that combine broad-scale citizen science platforms like GBIF with targeted provincial surveys. Such combined approaches are vital for addressing sampling biases, capturing both generalist and specialist taxa, and informing conservation strategies to safeguard Iran\u0026rsquo;s rich yet vulnerable Lepidoptera diversity (Beck et al. 2013; Chowdhury et al. 2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEcological Implications and Conservation Priorities\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of this study highlight the critical ecological role of specific Iranian ecoregions, such as the Caspian Hyrcanian mixed forests, Zagros Mountains forest steppe, and Elburz Range forest steppe, as biodiversity hotspots for Lepidoptera. These regions exhibit consistently high family richness, balanced family composition, and gradual cumulative increases in diversity over time, underscoring their importance as refugia for both generalist and specialist species. The mountainous topography and climatic stability of these areas provide favorable conditions for Lepidoptera to persist, adapt, and speciate (Harrison and Noss 2017; Hill et al. 2021). In contrast, arid and semi-arid regions such as the Registan-North Pakistan sandy desert and Baluchistan xeric woodlands face significant ecological challenges due to extreme abiotic conditions and limited habitat resources. These findings align with global patterns, where desert ecosystems often support lower biodiversity due to physiological constraints on ectothermic organisms like Lepidoptera (Bale et al. 2002; Kingsolver et al. 2011). The observed spatial mismatches between conservation priority hotspots and existing PAs (Noori et al. 2024) underline the urgent need to expand and upgrade Iran\u0026apos;s network of PAs to safeguard Lepidoptera biodiversity, especially in ecologically significant mountainous and forested regions. Furthermore, the pronounced effect of temperature on Lepidoptera family richness, coupled with the negative influence of excessive precipitation, emphasizes the vulnerability of these insects to climate change-induced habitat alterations.\u003c/p\u003e\n\u003cp\u003eThe ecological implications of these results call for immediate and region-specific conservation priorities to address the threats posed by climate change, habitat loss, and uneven sampling efforts. The substantial mismatch between conservation areas and biodiversity-rich regions, as observed in hotspots like the Alborz and Zagros mountain ranges, further exacerbates the risk of species decline (Rajaei et al. 2023; Noori et al. 2024). To mitigate these risks, targeted conservation strategies should prioritize the expansion of PAs and the enhancement of management efforts in biodiversity-rich regions, particularly those hosting endemic and specialist Lepidoptera. Additionally, standardized, long-term monitoring programs integrating broad-scale datasets such as GBIF with localized surveys are essential for filling data gaps and improving our understanding of biodiversity trends (Beck et al. 2013; Montgomery et al. 2021). Incorporating modern tools such as species distribution models (SDMs) and environmental DNA (eDNA) metabarcoding will enable more precise identification of critical habitats, while addressing anthropogenic pressures like overgrazing, habitat fragmentation, and land-use changes (Amiraslani and Dragovich 2011; Jowkar et al. 2016). Collectively, these measures are crucial for preserving Lepidoptera biodiversity in Iran, which serves as an essential indicator of ecosystem health and a vital component of ecological processes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe findings of this study highlight the critical need for targeted conservation efforts in biodiversity-rich ecoregions of Iran, particularly forested domains such as the Caspian Hyrcanian mixed forests and Zagros Mountains forest steppe, which exhibit consistently high Lepidoptera family richness. Equally important is addressing the significant sampling gaps in underrepresented arid and semi-arid ecoregions to ensure comprehensive biodiversity assessments. Integrating standardized data collection into global platforms like GBIF, complemented by localized surveys, can provide a more holistic understanding of Lepidoptera distribution and trends. Adaptive management strategies that mitigate the impacts of climate change by addressing temperature and precipitation variations are essential for safeguarding Lepidoptera as vital indicators of ecosystem health. By prioritizing these measures, conservation efforts can help preserve both the biodiversity and ecological resilience of Iran\u0026rsquo;s unique ecoregions in the face of ongoing environmental changes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmiraslani F, Dragovich D (2011) Combating desertification in Iran over the last 50 years: An overview of changing approaches. J Environ Manage 92:1\u0026ndash;13.\u003c/li\u003e\n\u003cli\u003eAntonelli A (2017) Biogeography: drivers of bioregionalization. Nat Ecol Evol 1:114.\u003cbr\u003e Bale JS, Masters GJ, Hodkinson ID, Wmack CA, Bezemer TM, Brown VK, Butterfield J, Buse A, Coulson JC, Farrar J, Good JEG, Harrington R, Hartley S, Jones TH, Lindroth RL, Press MC, Symrnioudis I, Watt AD, Whittaker JB (2002) Herbivory in global climate change research: direct effects of rising temperature on insect herbivores. Global Change Biol 8:1\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eBeck J, Ballesteros-Mejia L, Nagel P, Kitching IJ (2013) Online solutions and the \u0026lsquo;Wallacean shortfall\u0026rsquo;: What does GBIF contribute to our knowledge of species\u0026apos; ranges? Diversity Distrib 19:1043\u0026ndash;1050.\u003c/li\u003e\n\u003cli\u003eBeck J, B\u0026ouml;ller M, Erhardt A, Schwanghart W (2014) Spatial bias in the GBIF database and its effect on modeling species\u0026rsquo; geographic distributions. Ecol Inform 19:10\u0026ndash;15.\u003c/li\u003e\n\u003cli\u003eBrereton T, Roy D, Greatorex-Davieset N (2006) Thirty years and counting. The contribution to conservation and ecology of butterfly monitoring in the UK. Br Wildl 17:162\u0026ndash;170.\u003c/li\u003e\n\u003cli\u003eBuckley LB, Urban MC, Angilletta MJ, Crozier LG, Rissler LJ, Sears MW (2010) Can mechanism inform species distribution models? Ecol Lett 13:1041\u0026ndash;1054.\u003c/li\u003e\n\u003cli\u003eChowdhury S, Jennions MD, Zalucki MP, Maron M, Watson JE, Fuller RA (2022) Protected areas and the future of insect conservation. Trends Ecol Evol 38:85\u0026ndash;95.\u003c/li\u003e\n\u003cli\u003eCODATA, Pfeiffenberger H, Uhlir P, Hodson S (2020) Code for: Twenty-year review of GBIF. Zenodo. https://doi.org/10.35035/ctzm-hz97.\u003c/li\u003e\n\u003cli\u003eCrozier L (2003) Winter warming facilitates range expansion: cold tolerance of the butterfly Atalopedes campestris. Oecologia 135:648\u0026ndash;656.\u003c/li\u003e\n\u003cli\u003eDobson A, Lodge D, Alder J, Cumming GS, Keymer J, McGlade J, et al. (2006) Habitat loss, trophic collapse, and the decline of ecosystem services. Ecology 87:1915\u0026ndash;1924.\u003c/li\u003e\n\u003cli\u003eGBIF.org (2024), GBIF Occurrence Download. https://doi.org/10.15468/dl.jvyrzz, Accessed on 09 November 2024.\u003c/li\u003e\n\u003cli\u003eElith J, Leathwick JR (2009) Conservation prioritization using species distribution models. In: Moilanen A, Wilson KA, Possingham HP (eds) Spatial conservation prioritization: Quantitative methods and computational tools. Oxford University Press, Oxford, pp 70\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eFarashi A, Shariati M (2017) Biodiversity hotspots and conservation gaps in Iran. 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J Insect Conserv 1:149\u0026ndash;158.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-insect-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jico","sideBox":"Learn more about [Journal of Insect Conservation](http://link.springer.com/journal/10841)","snPcode":"10841","submissionUrl":"https://submission.nature.com/new-submission/10841/3","title":"Journal of Insect Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Climate change, Lepidoptera biodiversity, GBIF data, Iran ecoregions, biodiversity hotspots","lastPublishedDoi":"10.21203/rs.3.rs-5670034/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5670034/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study examined the impacts of climate change on Lepidoptera biodiversity across Iran’s ecoregions using Global Biodiversity Information Facility (GBIF), observational records, and long-term climate data from 1993 to 2022. Biodiversity metrics were analyzed, and Generalized Linear Model (GLM) analysis were used to identify key climatic and ecological drivers of family richness. Spatial analysis revealed a mean family richness of 5.89 (SD=7.13) and a mean Shannon-Wiener Index of 0.83 (SD=0.93) across ecoregions. Biodiversity hotspots, such as the Caspian Hyrcanian mixed forests, Zagros Mountains forest steppe, and Elburz Range forest steppe, exhibited consistently high family richness (19, 17, and 16 families, respectively) and balanced family compositions. In contrast, arid ecoregions, including the Registan-North Pakistan sandy desert and Baluchistan xeric woodlands suffered from insufficient sampling, limiting biodiversity assessments. Temporal analyses revealed forested regions had relatively complete accumulation curves while arid and semi-arid regions displayed uneven and incomplete sampling. Comparative analyses demonstrated that GBIF documented 27 unique families out of 70 families in observational records. Negative Binomial GLM showed that temperature positively influenced family richness, while higher precipitation negatively impacted family-level distribution. Comparative analysis between GBIF data and observational records revealed complementary strengths. GBIF provided broad-scale trends, while localized surveys uncovered specialist families often overlooked in global datasets. These findings emphasize the need to prioritize conservation efforts in biodiversity-rich ecoregions and addressing sampling gaps in underrepresented areas to mitigate climate change impacts on Lepidoptera in Iran.","manuscriptTitle":"Impacts of Climate Change on Lepidoptera Biodiversity in Iran: Insights from Long-Term Climate Data and GBIF Records","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-24 09:01:29","doi":"10.21203/rs.3.rs-5670034/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-10T18:50:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-31T07:31:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-03-23T23:26:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162490545316240729877085475555409308150","date":"2025-01-26T23:06:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"7720541671183428608110723056915121511","date":"2025-01-24T14:44:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-01-24T14:35:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-12-19T14:52:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-12-19T14:50:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Insect Conservation","date":"2024-12-18T13:38:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-insect-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jico","sideBox":"Learn more about [Journal of Insect Conservation](http://link.springer.com/journal/10841)","snPcode":"10841","submissionUrl":"https://submission.nature.com/new-submission/10841/3","title":"Journal of Insect Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"da1998aa-eca3-4be4-b5b6-e57a50e7cb7b","owner":[],"postedDate":"December 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-27T16:28:11+00:00","versionOfRecord":{"articleIdentity":"rs-5670034","link":"https://doi.org/10.1007/s10841-025-00723-2","journal":{"identity":"journal-of-insect-conservation","isVorOnly":false,"title":"Journal of Insect Conservation"},"publishedOn":"2025-10-23 16:17:22","publishedOnDateReadable":"October 23rd, 2025"},"versionCreatedAt":"2024-12-24 09:01:29","video":"","vorDoi":"10.1007/s10841-025-00723-2","vorDoiUrl":"https://doi.org/10.1007/s10841-025-00723-2","workflowStages":[]},"version":"v1","identity":"rs-5670034","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5670034","identity":"rs-5670034","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}