Environmental controls on butterfly occurrence and species richness in Israel: The importance of temperature over rainfall.

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.

InsectChange: a global database of temporal changes in insect and arachnid assemblages.

Insects are the most ubiquitous and diverse group of eukaryotic organisms on Earth, forming a crucial link in terrestrial and freshwater food webs. They have recently become the subject of headlines because of observations of dramatic declines in some places. Although there are hundreds of long-term insect monitoring programs, a global database for long-term data on insect assemblages has so far remained unavailable. In order to facilitate synthetic analyses of insect abundance changes, we compiled a database of long-term (≥10 yr) studies of assemblages of insects (many also including arachnids) in the terrestrial and freshwater realms. We searched the scientific literature and public repositories for data on insect and arachnid monitoring using standardized protocols over a time span of 10 yr or longer, with at least two sampling events. We focused on studies that presented or allowed calculation of total community abundance or biomass. We extracted data from tables, figures, and appendices, and, for data sets that provided raw data, we standardized trapping effort over space and time when necessary. For each site, we extracted provenance details (such as country, state, and continent) as well as information on protection status, land use, and climatic details from publicly available GIS sources. In all, the database contains 1,668 plot-level time series sourced from 165 studies with samples collected between 1925 and 2018. Sixteen data sets provided here were previously unpublished. Studies were separated into those collected in the terrestrial realm (103 studies with a total of 1,053 plots) and those collected in the freshwater realm (62 studies with 615 plots). Most studies were from Europe (48%) and North America (29%), with 34% of the plots located in protected areas. The median monitoring time span was 19 yr, with 12 sampling years. The number of individuals was reported in 129 studies, the total biomass was reported in 13 studies, and both abundance and biomass were reported in 23 studies. This data set is published under a CC-BY license, requiring attribution of the data source. Please cite this paper if the data are used in publications, and respect the licenses of the original sources when using (part of) their data as detailed in Metadata S1: Table 1.

Analysis of monitoring data where butterflies fly year-round.

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.