On the road: Anthropogenic factors drive the invasion risk of a wild solitary bee species

Complex biotic networks of invaders and their new environments pose immense challenges for researchers aiming to predict current and future occupancy of introduced species. This might be especially true for invasive bees, as they enter novel trophic interactions. Little attention has been paid to solitary, invasive wild bees, despite their increasing recognition as a potential global threat to biodiversity. Here, we present the first comprehensive species distribution modelling approach targeting the invasive bee Megachile sculpturalis, which is currently undergoing parallel range expansion in North America and Europe. While the species has largely colonised the most highly suitable areas of North America over the past decades, its invasion of Europe seems to be in its early stages. We showed that its current distribution is largely explained by anthropogenic factors, suggesting that its spread is facilitated by road and maritime traffic, largely beyond its intrinsic dispersal ability. Our results suggest that M. sculpturalis is likely to be negatively affected by future climate change in North America, while in Europe the potential suitable areas at-risk of invasion remain equally large. Based on our study, we emphasise the role of expert knowledge for evaluation of ecologically meaningful variables implemented and interpreted for species distribution modelling. We strongly recommend that the monitoring of this and other invasive pollinator species should be prioritised in areas identified as at-risk, alongside development of effective management strategies.

In situ adaptation and ecological release facilitate the occupied niche expansion of a non-native Madagascan day gecko in Florida

AIM

To investigate whether the frequently advocated climate-matching species distribution modeling approach could predict the well-characterized colonization of Florida by the Madagascar giant day gecko Phelsuma grandis.

LOCATION

Madagascar and Florida, USA.

METHODS

To determine the climatic conditions associated with the native range of P. grandis, we used native-range presence-only records and Bioclim climatic data to build a Maxent species distribution model and projected the climatic thresholds of the native range onto Florida. We then built an analogous model using Florida presence-only data and projected it onto Madagascar. We constructed a third model using native-range presences for both P. grandis and the closely related parapatric species P. kochi.

RESULTS

Despite performing well within the native range, our Madagascar Bioclim model failed to identify suitable climatic habitat currently occupied by P. grandis in Florida. The model constructed using Florida presences also failed to reflect the distribution in Madagascar by overpredicting distribution, especially in western areas occupied by P. kochi. The model built using the combined P. kochi/P. grandis dataset modestly improved the prediction of the range of P. grandis in Florida, thereby implying competitive exclusion of P. grandis by P. kochi from habitat within the former's fundamental niche. These findings thus suggest ecological release of P. grandis in Florida. However, because ecological release cannot fully explain the divergent occupied niches of P. grandis in Madagascar versus Florida, our findings also demonstrate some degree of in situ adaptation in Florida.

MAIN CONCLUSIONS

Our models suggest that the discrepancy between the predicted and observed range of P. grandis in Florida is attributable to either in situ adaptation by P. grandis within Florida, or a combination of such in situ adaptation and competition with P. kochi in Madagascar. Our study demonstrates that climate-matching species distribution models can severely underpredict the establishment risk posed by non-native herpetofauna.

Assessing temporal trends of trace metal concentrations in mosses over France between 1996 and 2011: A flexible and robust method to account for heterogeneous sampling strategies

Air quality biomonitoring has been successfully assessed using mosses for decades in Europe, particularly regarding heavy metals (HM). Assessing robust temporal variations of HM concentrations in mosses requires to better understand to what extent they are affected by the sampling protocol and the moss species. This study used the concentrations of 14 elements measured during four surveys over 15 years in France. Analyses of variance (ANOVA) and a modeling approach were used to decipher temporal variations for each element and adjust them with parameters known to affect concentrations. ANOVA followed by post hoc analyses did not allow to estimate clear trends. A generalized additive mixed modeling approach including the sampling period, the collector and the moss species, plus quadratic effects, was used to analyze temporal variations on repeated sampling sites. This approach highlighted the importance of accounting for non-linear temporal variations in HM, and adjusting for confounding factors such as moss species, species-specific differences between sampling periods, collector and methodological differences in sampling campaigns. For instance, lead concentrations in mosses decreased between 1996 and 2011 following quadratic functions, with faster declines for the most contaminated sites in 1996. On the other hand, other HM showed double trends with U-shaped or hill-shaped curves. The effect of the moss was complex to handle and our results advocate for using one moss species by repeated site to better analyze temporal variations.