Overcoming data limitations in macrogenetics: assessing and accounting for spatial heterogeneity in sequence data

Paul, M. J.; Moreira, M. O.; Liz, A. V.; Hanson, J. O.; Tarroso, P.; Crottini, A.; Goncalves, D. V.; Carnaval, A. C.; Carstens, B. C.; Moritz, C.; Carvalho, S. B.

doi:10.64898/2026.09.29.755480

bioRxiv · 10.64898/2026.09.29.755480

Overcoming data limitations in macrogenetics: assessing and accounting for spatial heterogeneity in sequence data

Paul, M. J.·Moreira, M. O.·Liz, A. V.·Hanson, J. O.·Tarroso, P.·Crottini, A.·Goncalves, D. V.·Carnaval, A. C.·Carstens, B. C.·Moritz, C.·Carvalho, S. B.

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Abstract

At large scales, our ability to compare genetic diversity across species is often challenged by the difficulty of extracting range-wide sequence data from online databases. Using mitochondrial DNA from amphibians and reptiles spanning across six regions: Brazilian Atlantic Forest, Iberian Peninsula, northwestern Africa, Arabian Peninsula, Madagascar and Australian Monsoonal Tropics, we develop and implement a framework to overcome common limitations in macrogenetics. We compiled and manually curated a spatially explicit genetic dataset from public repositories, quantified the spatial representativeness of genetic sampling across species' ranges and developed a novel interpolation framework to estimate and map species-specific patterns of intraspecific genetic diversity (ISD) while accounting for heterogeneous spatial sampling. We estimated mean nucleotide diversity across species to produce regional maps for each genetic marker. Our final dataset comprised 182 amphibian and 239 reptile species, encompassing 184 genera from 54 families. Range representativeness of genetic sampling was generally high, despite some variation across study regions. Spatial patterns of ISD revealed geographic heterogeneity, and were consistent across the best-sampled mitochondrial markers. By combining automated and manual georeferencing, quantifying range representativeness, and accounting for bias in spatial interpolations, our pipeline is marker-agnostic and directly applicable to nuclear and genomic data as they become available.

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Paul, M. J., Moreira, M. O., Liz, A. V., Hanson, J. O., Tarroso, P., Crottini, A., Goncalves, D. V., Carnaval, A. C., Carstens, B. C., Moritz, C., Carvalho, S. B.. 2026-10-02. Overcoming data limitations in macrogenetics: assessing and accounting for spatial heterogeneity in sequence data. https://doi.org/10.64898/2026.09.29.755480

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