Roy Snelling

Species exhibit a range of specialisation in diet and other niche axes, with specialists typically thought to be more efficient in resource use but more vulnerable to extinction than generalists. Among herbivorous insects, dietary specialists seem more likely to lack acceptable host plants during the insect's feeding stage, owing to fluctuations in host‐plant abundance or phenology. Like other herbivores, bee species vary in host breadth from pollen specialisation (oligolecty) to generalisation (polylecty).Several studies have shown greater interannual variation in flowering phenology for earlier‐flowering plants than later‐flowering plants, suggesting that early‐season bees may experience substantial year‐to‐year variation in the floral taxa available to them.It was therefore reasoned that, among bees, early phenology could be a more viable strategy for generalists, which can use resources from multiple floral taxa, than for specialists. Consequently, it was expected that the median dates of collection of adult specimens to be earlier for generalist species than for specialists. To test this, phenology data and pollen diet information on 67 North American species of the bee genus Osmia was obtained.Controlling for latitude and phylogeny, it was found that dietary generalisation is associated with significantly earlier phenology, with generalists active, on average, 11–14 days earlier than specialists.This result is consistent with the generalist strategy being more viable than the specialist strategy for species active in early spring, suggesting that dietary specialisation may constrain the evolution of bee phenology—or vice versa.

Recent advances in species distribution models (SDMs) associated with artificial intelligence (AI) and increased volumes of available data for model variables have allowed reliable evaluation of the potential distribution of any species. A reliable SDM requires suitable occurrence records and variables with optimal model structures. In this study, we developed three different machine learning-based SDMs [MaxEnt, random forest (RF), and multi-layer perceptron (MLP)] to predict the global potential distribution of two invasive ants under current and future climates. These SDMs showed that the potential distribution of Solenopsis invicta would be expanded by climatic change, whereas it would not significantly change for Anoplolepis gracilipes. The models were compared using model performance metrics, and the optimal model structure and spatial projection were selected. The MaxEnt exhibited high performance, while the MLP model exhibited low performance, with the largest variation by climate change. Random forest showed the smallest potential distribution area, but it was robust considering the number of occurrence records and changes in model variables. All the models showed reliable performance, but the difference in performance and projection size suggested that optimal model selection based on data availability, model variables, study objectives, or an ensemble approach was necessary to develop a comprehensive SDM to minimize modeling uncertainty. We expect that this study will help with the use of AI-based SDMs for the evaluation and risk assessment of invasive ant species.

BACKGROUND: The bean bug Riptortus pedestris has received intense attention in recent years because of its involvement in increasing outbreaks of the staygreen syndrome in soybean (Glycine max (L.)) often causing almost 100% losses of soybean yield in China. However, for this pest of great economic importance, the potential current and future distribution patterns and their underlying driving factors remain unclear. RESULTS: The Maxent modelling under climate, elevation and land-use (including the distribution information of Glycine max) variables showed the current potential distribution covered a vast geographic range, primarily including most parts of South, Southeast and East Asia. Under future environmental scenarios, the suitable habitat was markedly expanded. The areas to newly become highly suitable for R. pedestris were primarily located in Northeast China and West India. Five bioclimatic (BIO13, BIO08, BIO18, BIO02 and BIO07) and one land-use (C3 annual crops) predictors contributed approximately 95% to the modelling, and analyses of curve responses showed that R. pedestris preferred relatively high temperature and precipitation to a certain degree. Our results indicate that a high risk of R. pedestris outbreaks is present in parts of Asia, especially in the growing regions of soybean in China, and this risk will continue in the future. CONCLUSION: The predicted distribution pattern and key regulating factors identified herein could provide a vital reference for developing policies in pest management and further alleviate the incidence of staygreen syndrome in soybean.

(no abstract available)

Aim Aggregated species occurrence data are increasingly accessible through public databases for the analysis of temporal trends in the geographic distributions of species. However, biases in these data present challenges for statistical inference. We assessed potential biases in data available through GBIF on the occurrences of four flower-visiting taxa: bees (Anthophila), hoverflies (Syrphidae), leaf-nosed bats (Phyllostomidae) and hummingbirds (Trochilidae). We also assessed whether and to what extent data mobilization efforts improved our ability to estimate trends in species' distributions. Location The Neotropics. Methods We used five data-driven heuristics to screen the data for potential geographic, temporal and taxonomic biases. We began with a continental-scale assessment of the data for all four taxa. We then identified two recent data mobilization efforts (2021) that drastically increased the quantity of records of bees collected in Chile available through GBIF. We compared the dataset before and after the addition of these new records in terms of their biases and estimated trends in species' distributions. Results We found evidence of potential sampling biases for all taxa. The addition of newly-mobilized records of bees in Chile decreased some biases but introduced others. Despite increasing the quantity of data for bees in Chile sixfold, estimates of trends in species' distributions derived using the postmobilization dataset were broadly similar to what would have been estimated before their introduction, albeit more precise. Main conclusions Our results highlight the challenges associated with drawing robust inferences about trends in species' distributions using publicly available data. Mobilizing historic records will not always enable trend estimation because more data do not necessarily equal less bias. Analysts should carefully assess their data before conducting analyses: this might enable the estimation of more robust trends and help to identify strategies for effective data mobilization. Our study also reinforces the need for targeted monitoring of pollinators worldwide.

Earth's climate is on track to surpass the proposed mean global temperature change limit of 1.5ºC above pre-industrial levels, threatening to disrupt ecosystems globally. Yet, studies on temperate bee response to climate change are limited, with most studies of non-Apis bees focusing on the eusocial genus Bombus. Here, we assess the response of a rare habitat and host plant specialist bee, Macropis nuda, to projected climate change scenarios. We use species distribution models of M. nuda and its host plant, Lysimachia ciliata, trained on publicly available occurrence records, to evaluate bee distribution and habitat suitability changes under four climate change scenarios. We find that the bee and host plant distributions respond synchronously to increased greenhouse gas emissions, which result in range-wide habitat suitability loss and a northward range shift. These results provide an important example of a temperate solitary bee's response to climate change and help inform conservation efforts to preserve pollinator biodiversity and pollinator-host plant relationships.

For many regions, such as in South America, it is unclear how well the existent protected areas network (PAs) covers different taxonomic groups and if there is a coverage bias of PAs towards certain biomes or species. Publicly available occurrence data along with ecological niche models might help to overcome this gap and to quantify the coverage of taxa by PAs ensuring an unbiased distribution of conservation effort. Here, we use an occurrence database of 271 species from the cacao family (Malvaceae) to address how South American PAs cover species with different distribution, abundance, and threat status. Furthermore, we compared the performance of online databases, expert knowledge, and modelled species distributions in estimating species coverage in PAs. We found 79 species from our survey (29% of the total) lack any record inside South American PAs and that 20 out of 23 species potentially threatened with extinction are not covered by PAs. The area covered by South American PAs was low across biomes, except for Amazonia, which had a relative high PA coverage, but little information on species distribution within PA available. Also, raw geo-referenced occurrence data were underestimating the number of species in PAs, and projections from ecological niche models were more prone to overestimating the number of species represented within PAs. We discuss that the protection of South American flora in heterogeneous environments demand for specific strategies tailored to particular biomes, including making new collections inside PAs in less collected areas, and the delimitation of more areas for protection in more known areas. Also, by presenting biasing scenarios of collection effort in a representative plant group, our results can benefit policy makers in conserving different spots of tropical environments highly biodiverse.

Abstract Rodent biogeographic studies are disproportionately scarce in Iran, however, they are an ideal system to understand drivers of biodiversity distributions in the country. The aims of the present research are to determine (i) the pattern of rodent richness across the country, (ii) quantify th…

The ever-increasing human population, globalisation, and desire to keep pets have resulted in the translocation of many species into non-native environments. As a result, some of the non-native reptile species have been introduced to South Africa through the pet trade. However, little is known about…

As hybrid zones exhibit selective patterns of gene flow between otherwise distinct lineages, they can be especially valuable for informing processes of microevolution and speciation. The bumble bee, Bombus melanopygus, displays two distinct color forms generated by Müllerian mimicry: a northern “Roc…