Landscape genomics to the rescue of a tropical bee threatened by habitat loss and climate change

Abstract

Habitat degradation and climate change are currently threatening wild pollinators, compromising their ability to provide pollination services to wild and cultivated plants. Landscape genomics offers powerful tools to assess the influence of landscape modifications on genetic diversity and functional connectivity, and to identify adaptations to local environmental conditions that could facilitate future bee survival. Here, we assessed range‐wide patterns of genetic structure, genetic diversity, gene flow, and local adaptation in the stingless bee Melipona subnitida, a tropical pollinator of key biological and economic importance inhabiting one of the driest and hottest regions of South America. Our results reveal four genetic clusters across the species’ full distribution range. All populations were found to be under a mutation–drift equilibrium, and genetic diversity was not influenced by the amount of reminiscent natural habitats. However, genetic relatedness was spatially autocorrelated and isolation by landscape resistance explained range‐wide relatedness patterns better than isolation by geographic distance, contradicting earlier findings for stingless bees. Specifically, gene flow was enhanced by increased thermal stability, higher forest cover, lower elevations, and less corrugated terrains. Finally, we detected genomic signatures of adaptation to temperature, precipitation, and forest cover, spatially distributed in latitudinal and altitudinal patterns. Taken together, our findings shed important light on the life history of M. subnitida and highlight the role of regions with large thermal fluctuations, deforested areas, and mountain ranges as dispersal barriers. Conservation actions such as restricting long‐distance colony transportation, preserving local adaptations, and improving the connectivity between highlands and lowlands are likely to assure future pollination services.

Ecological, genetic and evolutionary drivers of regional genetic differentiation in Arabidopsis thaliana

“…IBD was based on geographic distance. However, instead of using the Euclidian distance among population pairs, we created a raster by assigning a 0.5 value to all 1-km 2 pixels [90,91] of the Iberian Peninsula map. We then calculated pairwise distances, known as geographic resistance distance, among all A. thaliana populations employing the new raster.…”

“…Given the inherent spatial dependence structure in pairwise comparisons between A. thaliana populations, all MLPE considered spatial locations. To this end, we used a modification of the MLPE model incorporating the correlation between pairwise measurements due to comparison of populations and spatial locations (nested MLPE or NMLPE) [91]. Since the independent variables (IBD, IBE and IBR) used in NMLPE had very different units, we normalized them by subtracting the mean and dividing by the standard deviation in all analyses.…”

Genetic structure across urban and agricultural landscapes reveals evidence of resource specialization and philopatry in the Eastern carpenter bee, Xylocopa virginica L.

“…In this case, we assigned developed land, cultivated land, and forest habitats a resistance value of 0.1 and all other land cover types within the study area a value of 0.9. Finally, we tested a set of hypotheses (Set C) that single land uses are independently driving gene flow, with each of the land‐use types in Hypothesis B as single terms, with each assigned a resistance value of 0.1, and all other land‐use types assigned a resistance value of 0.9 (Jaffé et al., 2019; as per Jha, 2015). To test for any nonlinear relationships between genetic distance and land use, we also created three additional resistance maps per hypothesis, varying the low and high resistance values between 0.1–0.3 and 0.5–0.9, respectively (sensu Jha & Kremen, 2013b; Ortego, Aguirre, Noguerales, & Cordero, 2015; Zellmer & Knowles, 2009).…”

Alternative Quantifications of Landscape Complementation to Model Gene Flow in Banded Longhorn Beetles [Typocerus v. velutinus (Olivier)]

“…Explanatory variables met normality assumptions and were not transformed, but they were scaled prior to modeling. We conducted a test for residual spatial autocorrelation for the full MLPE model for each type of metrics (Jaffé et al, 2019) and found no significant autocorrelation. Because the same sampling sites were considered in each submodel, this satisfies the spatial independence assumption for all submodels, i.e., model selection with subsets of the predictors included in the full model.…”