RESUMO
Butterflies are considered important indicators representing the state of biodiversity and key ecosystem functions, but their use as bioindicators requires a better understanding of how their observed response is linked to environmental factors. Moreover, better understanding how butterfly faunas vary with climate and land cover may be useful to estimate the potential impacts of various drivers, including climate change, botanical succession, grazing, and afforestation. It is particularly important to establish which species of butterflies are sensitive to each environmental driver. The study took place in Israel, including the West Bank and Golan Heights. To develop a robust and systematic approach for identifying how butterfly faunas vary with the environment, we analyzed the occurrence of 73 species and the abundance of 24 species from Israeli Butterfly Monitoring Scheme (BMS-IL) data. We used regional generalized additive models to quantify butterfly abundance, and generalized linear latent variable models and generalized linear models to quantify the impact of temperature, rainfall, soil type, and habitat on individual species and on the species community. Species richness was higher for cooler transects, and also for hilly and mountainous transects in the Mediterranean region (rendzina and Terra rossa soils) compared with the coastal plain (Hamra soil) and semiarid northern Jordan Vale (loessial sierozem soil). Species occurrence was better explained by temperature (negative correlation) than precipitation, while for abundance the opposite pattern was found. Soil type and habitat were insignificant drivers of occurrence and abundance. Butterfly faunas responded very strongly to temperature, even when accounting for other environmental factors. We expect that some butterfly species will disappear from marginal sites with global warming, and a large proportion will become rarer as the region becomes increasingly arid.
RESUMO
Butterfly Monitoring Schemes (BMSs) engage the public in conservation and provide data sets that cover broad geographical areas over long timescales. Most existing BMSs are in temperate climates; however, the Israeli Butterfly Monitoring Scheme (BMS-IL), established in 2009, is a notable exception as it encompasses a large climatic gradient from Euro-Siberian through Mediterranean to hyper-arid regions. Israel's climate poses challenges in analyzing data from year-round butterfly activity, as in other tropical or arid countries. The Regional Generalized Additive Model (Regional GAM) is a butterfly phenology and abundance model based on repeat visits throughout species' flight season. We tested the applicability of Regional GAM for species with complex flight seasonality (e.g., multivoltine) by comparing estimated abundance and seasonal indices for the full data set and rarefied subsets. We assessed the reliability of modeled flight seasons and compared abundance estimates per site resulting from biologically plausible and unreliable seasonal models. The reliability of Regional GAM rises with the number of observations, and the model tends to produce more biologically plausible models for species with simple phenologies (e.g., univoltine with a single peak in activity). Abundance estimates based on unreliable models produce values with inter-quartile ranges of 90%-153% compared with biologically plausible models, while peak time changes with an interquartile range of 0-22.5 d when comparing all rarefied models with the full data set. Regional GAM should be applied with great caution for rare species and those with a complex flight season, and the date of year start needs to be carefully chosen for species that are active year-round. We identified the key sources of error and propose an operational workflow to address them. With few adaptations, Regional GAM can support new BMSs in analyzing data where butterflies are active year-round, including tropical climates. We propose guidelines for analyzing BMS data for species or regions with long activity periods and complex phenologies.
