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      New insights into morphological adaptation in common mole‐rats ( Cryptomys hottentotus hottentotus) along an aridity gradient

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          Abstract

          Morphological adaptation is the change in the form of an organism that benefits the individual in its current habitat. Mole‐rats (family Bathyergidae), despite being subterranean, are impacted by both local and broad‐scale environmental conditions that occur above ground. Common mole‐rats ( Cryptomys hottentotus hottentotus) present an ideal mammalian model system for the study of morphological variation in response to ecology, as this species is found along an aridity gradient and thus can be sampled from geographically non‐overlapping populations of the same species along an environmental longitudinal cline. Using the mass of five internal organs, ten skeletal measurements and 3D morphometric analyses of skulls, we assessed the morphology of wild non‐breeding individuals from five common mole‐rat populations in South Africa. We found that the body mass and mean relative mass of the spleen and kidneys in arid populations was larger, and individuals from arid regions possessed shorter legs and larger inter‐shoulder widths compared to individuals from mesic regions. Additionally, arid populations demonstrated greater skull depth, and shape change of features such as angular processes of the lower jaw than mesic individuals, indicating that these distinct geographic populations show differences corresponding to the aridity gradient, potentially in response to environmental factors such as the variation in food sources found between different habitats, in addition to different soil compositions found in the different regions. Arid populations potentially require a stronger jaw and neck musculature associated with mastication to chew xeric‐adapted plants and to dig through hard soil types, whereas mesic populations excavate through soft, looser soil and may make use of their front limbs to aid the movement of soils when digging. Aridity influences the morphology of this species and could indicate the impact of environmental changes on speciation and mammalian skull morphology.

          Abstract

          Distinct geographic populations of South African mole‐rats showed morphological variation that matched the predictions of the Aridity Index theory in response to factors such as the variation in food sources found in different biomes, as well as soil composition found in the different regions. Arid populations require a stronger jaw and neck musculature associated with mastication of xeric‐adapted plants, and to dig through the harder and rocky soil types. In contrast, mesic populations excavate through soft, looser soil and may make use of their front limbs to aid the movement of soil when digging. These findings are novel and suggest that ecological factors can influence the morphology within a species and could suggest the impact of environmental changes on speciation and mammalian skull morphology.

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          MorphoJ: an integrated software package for geometric morphometrics.

          Increasingly, data on shape are analysed in combination with molecular genetic or ecological information, so that tools for geometric morphometric analysis are required. Morphometric studies most often use the arrangements of morphological landmarks as the data source and extract shape information from them by Procrustes superimposition. The MorphoJ software combines this approach with a wide range of methods for shape analysis in different biological contexts. The program offers an integrated and user-friendly environment for standard multivariate analyses such as principal components, discriminant analysis and multivariate regression as well as specialized applications including phylogenetics, quantitative genetics and analyses of modularity in shape data. MorphoJ is written in Java and versions for the Windows, Macintosh and Unix/Linux platforms are freely available from http://www.flywings.org.uk/MorphoJ_page.htm. © 2010 Blackwell Publishing Ltd.
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            R: A Language And Environment For Statistical Computing

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              Multimodel inference in ecology and evolution: challenges and solutions.

              Information theoretic approaches and model averaging are increasing in popularity, but this approach can be difficult to apply to the realistic, complex models that typify many ecological and evolutionary analyses. This is especially true for those researchers without a formal background in information theory. Here, we highlight a number of practical obstacles to model averaging complex models. Although not meant to be an exhaustive review, we identify several important issues with tentative solutions where they exist (e.g. dealing with collinearity amongst predictors; how to compute model-averaged parameters) and highlight areas for future research where solutions are not clear (e.g. when to use random intercepts or slopes; which information criteria to use when random factors are involved). We also provide a worked example of a mixed model analysis of inbreeding depression in a wild population. By providing an overview of these issues, we hope that this approach will become more accessible to those investigating any process where multiple variables impact an evolutionary or ecological response. © 2011 The Authors. Journal of Evolutionary Biology © 2011 European Society For Evolutionary Biology.
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                Author and article information

                Contributors
                hana.merchant.2020@live.rhul.ac.uk
                Journal
                Ecol Evol
                Ecol Evol
                10.1002/(ISSN)2045-7758
                ECE3
                Ecology and Evolution
                John Wiley and Sons Inc. (Hoboken )
                2045-7758
                21 April 2024
                April 2024
                : 14
                : 4 ( doiID: 10.1002/ece3.v14.4 )
                : e11301
                Affiliations
                [ 1 ] Department of Biological Sciences, School of Life and Environmental Sciences Royal Holloway University of London Egham, Surrey UK
                [ 2 ] School of Biological and Behavioural Sciences Queen Mary University of London London UK
                [ 3 ] Department of Zoology and Entomology University of Pretoria Pretoria Gauteng South Africa
                [ 4 ] Faculty of Science, Technology, Engineering, and Mathematics Open University Milton Keynes UK
                Author notes
                [*] [* ] Correspondence

                Hana N. Merchant, Department of Biological Sciences, School of Life and Environmental Sciences, Royal Holloway University of London, Egham, Surrey TW20 0EX, UK.

                Email: hana.merchant.2020@ 123456live.rhul.ac.uk

                Author information
                https://orcid.org/0000-0003-1373-8808
                Article
                ECE311301 ECE-2023-11-02084.R1
                10.1002/ece3.11301
                11033624
                38651162
                f92cc78a-ef49-4676-ab3f-848a092906e4
                © 2024 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 March 2024
                : 27 November 2023
                : 05 April 2024
                Page count
                Figures: 7, Tables: 5, Pages: 15, Words: 11000
                Funding
                Funded by: Natural Environment Research Council , doi 10.13039/501100000270;
                Award ID: NE\L002485\1
                Categories
                Evolutionary Ecology
                Research Article
                Research Articles
                Custom metadata
                2.0
                April 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.0 mode:remove_FC converted:22.04.2024

                Evolutionary Biology
                evolution,geometric morphometrics,local adaptation,shape analysis
                Evolutionary Biology
                evolution, geometric morphometrics, local adaptation, shape analysis

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