TY - JOUR
AU1 - Miszkiewicz, Justyna, J
AU2 - Louys,, Julien
AU3 - Beck, Robin M, D
AU4 - Mahoney,, Patrick
AU5 - Aplin,, Ken
AU6 - O’Connor,, Sue
AB - Abstract Skeletal growth rates reconstructed from bone histology in extinct insular hippopotamids, elephants, bovids and sauropods have been used to infer dwarfism as a response to island conditions. Limited published records of osteocyte lacunae densities (Ot.Dn), a proxy for living osteocyte proliferation, have suggested a slower rate of bone metabolism in giant mammals. Here, we test whether insularity might have affected bone metabolism in a series of small to giant murine rodents from Timor. Ten adult femora were selected from a fossil assemblage dated to the Late Quaternary (~5000–18 000 years old). Femur morphometric data were used in computing phylogenetically informed body mass regressions, although the phylogenetic signal was very low (Pagel’s λ = 0.03). Estimates of body weight calculated from these femora ranged from 75 to 1188 g. Osteocyte lacunae densities from histological sections of the midshaft femur were evaluated against bone size and estimated body weight. Statistically significant (P < 0.05) and strongly negative relationships between Ot.Dn, femur size and estimated weight were found. Larger specimens were characterized by lower Ot.Dn, indicating that giant murines from Timor might have had a relatively slow pace of bone metabolic activity, consistent with predictions made by the island rule. bone histology, gigantism, insularity, Murinae, osteocyte lacunae, Late Pleistocene, Late Quaternary Introduction Island ecology and biogeography have long served as models for investigating species richness, extinction, speciation, conservation and evolutionary biology (Brown & Kodric-Brown, 1977; Whittaker & Fernández-Palacios, 2007; Sax & Gaines, 2008; MacArthur & Wilson, 2016). Islands are ideal examples of isolated ecosystems that can trigger similar behavioural and biological responses across different animals (Whittaker & Fernández-Palacios, 2007; Miller & Spoolman, 2011; MacArthur & Wilson, 2016). Foster (1964) was the first to discuss changes in body size in species affected by insularity. Van Valen (1973) formalized this under the island rule, which is now accepted as one of the most fundamental theories in evolutionary biology (Clegg & Owens, 2002; Schillaci et al., 2009; Benton et al., 2010). It posits that large and small insular mammals decrease and increase their body size, respectively, to accommodate resource availability and drive optimal life histories. Sondaar (1977) then provided a broader perspective on mammal insularity and diversification, highlighting the need to consider islands based on their ‘oceanic and continental’ (p. 617) origin, and to study each one within its own context owing to complex island histories. Lomolino’s (1985; Lomolino et al., 2013) later re-examination and redefinition of the island rule specifically encompassed a dwarfism–gigantism gradient (for review, see Lokatis & Jeschke, 2018). Some issues relating to biological constraints limiting the plasticity of a species (Meiri et al., 2004, 2008), otherwise known as phylogenetic inertia (Darwin, 1859), have since also been considered. Inferring the cause of body size change in relationship to insularity has been subject to much discussion (e.g. Lomolino, 1985, 2013; Millien & Damuth, 2004; Meiri et al., 2004, 2006; Itescu et al., 2014; Faurby & Svenning, 2016). Trends in body size changes on islands are often associated with data scatter, which are likely to represent multiple factors contributing to the body mass of an animal. These factors include inter-island differences in competition for resources and mating opportunities, resource availability, geographical factors such as island size and distance from other islands or the mainland, latitude and climate (McNab, 1971, 2010). In cases of adaptive radiation from a single ancestor, it is also possible for organisms to diversify rapidly into both giant and small forms. Only in conditions of ecological release and sufficient time in isolation could body mass trend lines be fitted perfectly (Lomolino, 2005). When applying the island rule to birds and mammals, which have high resource requirements associated with high metabolic rates in comparison to other terrestrial vertebrates, body mass is a good indicator of life history and energetic investment (McNab, 2019). Body mass closely reflects the basal metabolic rate in endotherms, which generate and regulate heat internally to satisfy the energetic demands of survival and reproduction (McNab, 2019). Body mass measures, including estimates from fossil material, and large-scale meta-analyses have demonstrated gigantism and dwarfism in multiple species globally (Yabe, 1994; Boback & Guyer, 2003; Lomolino, 2005; Palombo, 2007; Köhler & Moyà-Solà, 2009; van der Geer et al., 2013). Histological techniques, in particular, have also proved valuable in reconstructing metabolic activity of the once-living bone tissues of different species and taxa by capturing cell metabolic activity indicators preserved in their fossils (e.g. Köhler & Moyà-Solà, 2009; Benton et al., 2010; Orlandi-Oliveras et al., 2016). The island rule and rodents Island rodents, particularly mice, rats and related species (superfamily Muroidea), have been of particular interest for addressing physiological, morphological and behavioural responses to island ecology (Adler & Levins, 1994; Renaud & Millien, 2001; Abdelkrim et al., 2005; Harper et al., 2005; Towns et al., 2006; Firmat et al., 2010; Moncunill-Solé et al., 2014; Swift et al., 2018; van der Geer, 2018; Geffen & Yom-Tov, 2019). Owing to their relatively short lifespans, high level of reproduction and multiple adaptive radiations, they are important models for studying the environmental plasticity of animals (Miszkiewicz et al., 2019; van der Geer, 2019). Comparisons between insular and mainland rodent populations have focused on reproductive behaviour (Stamps & Buechner, 1985) and morphology (Lomolino, 1984), collectively termed the ‘island syndrome’ (Adler & Levins, 1994; Adler, 1996; Russell et al., 2011). Isolated insular rodent populations experience a demographic increase in density and dispersal, improved survival and associated reproduction rates, minimized interspecific competition, and an increase in body mass the more isolated and smaller the island (Foster, 1964). However, there have also been cases of insular rodents that have evolved into dwarfed forms (e.g. Perognathus spp. on islands bordering Mexico) owing to limitations in food supply in heterogeneous environments (Lawlor, 1982; Durst & Roth, 2015). Adaptive shifts in rodent morphology and/or behaviour are short or long term depending on the time scale, sample and context investigated (Palkovacs, 2003). Rodent size adaptation probably occurs initially as a short-term phenotypic change in response to increased island population density. Natural selection favouring increased body size would follow on a longer time scale when mortality rates and predation are stable and low, as they are on islands (Brown & Sibly, 2006). Foster’s (1964) report of insular mammal gigantism was based on observations of two species of deer mice (Peromyscus maniculatus and Peromyscus sitkensis) of the Queen Charlotte Islands in Canada. Almost double the size of Peromyscus maniculatus, Peromyscus sitkensis was found on the outer small and dispersed islands. Foster (1964) suggested that a depauperate fauna, reduced competition for resources and minimized predation on small islands favoured insular gigantism as a selective advantage. Empirical evidence for a change in rodent body mass has since been reported for several other species spanning many geographical locations (e.g. Ventura & Fuster, 2000; Michaux et al., 2002; Millien & Damuth, 2004; Russell et al., 2011; Pergams et al., 2015). An increase in body size on small islands has been observed in Japanese Apodemus speciosus (Millien & Damuth, 2004), black rats (Rattus rattus) in the Mozambique Channel (Russell et al., 2011), Polynesian rats (Rattus exulans) and Rattus rattus in New Zealand and the Pacific islands (Yom-Tov et al., 1999), Californian Rattus rattus from Anacapa Island (Pergams et al., 2015), the woodmouse (Apodemus sylvaticus) in the Western Mediterranean Sea (Michaux et al., 2002) and Rattus rattus from Congreso Island in Spain (Ventura & Fuster, 2000). Literature on the skeletal biology of island fossil rats mostly reports gross anatomy and morphometric data used for taxonomic purposes. Measurements of dental material (Millien & Damuth, 2004; Louys et al., 2018) and cranial and postcranial morphology (Bocherens et al., 2006; Aplin & Helgen, 2010) have been used in taxonomic assignments, but these data have proved equally informative about locomotion, diet and ecology of rodents, as in the case of a now well-studied extinct giant genus, Mikrotia, from the Gargano peninsula (Zafonte & Masini, 1992; Parra et al., 1999; Moncunill-Solé et al., 2018). Very large, insular members of the murid subfamily Murinae have been reported from the fossil record at multiple locations throughout the world, including the Flores giant rat (Papagomys armandvillei) in Indonesia (Locatelli et al., 2012), Coryphomys from Timor (Aplin & Helgen, 2010) and the Tenerife giant rat (Canariomys bravoi) from the Canary Islands (Bocherens et al., 2006; Firmat et al., 2011). Megalomys is a member of another muroid family, Cricetidae, and is known from five very large species from the West Indies (van den Hoek Ostende et al., 2017). Some extant giant muroid species that had colonized their islands in the Late Pleistocene or earlier include Diplothrix legata, Apodemus speciosus and Apodemus argenteus in Japan (Kawamura, 1991), Phloeomys cumingi and Phloeomys pallidus in the Philippines (Rickart & Heaney, 2002) and Hypogeomys antimena in Madagascar (Sommer et al., 2002). Bone histology and insular fossil animals Histological sectioning of fossil bone has proved successful for the reconstruction of tetrapod palaeobiology (Chinsamy-Turan, 2011; de Ricqlès, 2011). By studying microscopic structures and composition in bone samples of fossil vertebrates, skeletal maturation, seasonality, behaviour and bone metabolism can be reconstructed (Chinsamy-Turan, 2011; Köhler et al., 2012). As bone tissue forms, matures and remodels throughout the lifespan of an animal, this information is reflected in the density, organization, morphology and geometric properties of bone microstructure (Enlow & Brown, 1956, 1957, 1958). This approach has been applied successfully in insularity contexts (for review, see Kolb et al., 2015). For example, slow bone growth rates indicate delayed maturity and extended lifespans in the Late Pleistocene dwarfed Balearic island ‘goat’ (Myotragus balearicus) (Köhler, 2010; Köhler & Moyà-Solà, 2009) and insular dwarfism in the Late Jurassic sauropod Europasaurus holgeri (Sander et al., 2006). To the best of our knowledge, quantitative palaeohistological analyses in relationship to island ecology have not been performed for island fossil rodents. Prior research in extinct giant rodent cases reported bone tissue only in the Late Miocene murid Mikrotia magna from Gargano Island in Italy (Kolb et al., 2015). We also recently (Miszkiewicz et al., 2019) reported descriptions of bone remodelling in one of the giant murines (ANU TDS 0‐30 #4) in comparison to a small murine femur (ANU TDD 1 #11) from the same assemblage analysed in the present research. Orlandi-Oliveras et al. (2016) observed bone histology of the fossil giant dormouse Hypnomys onicensis (Gliridae) from the Balearic Islands indicative of increased lifespan that might have been a result of gigantism. Although these previous studies have included the description of bone tissue types and their organization, the quantification of osteocyte lacunae within the bone matrix in relationship to insularity remains to be tested. Prior research exploring osteocyte lacunae densities (Ot.Dn) has revealed relationships between this measure of bone metabolic activity and negative relationships with body size in non-insular settings that might ultimately be linked to aspects of life history. Interspecific studies of fast-maturing and small-bodied and of slow-maturing and large-bodied mammal species exhibit higher and lower osteocyte densities, respectively (Mullender et al., 1996; Bromage et al., 2009). This phenomenon might reflect an underlying complex relationship between bone ontogeny, rates of metabolism and cell proliferation that are related to body mass (Bromage et al., 2009). For example, Ot.Dn decreased with increased body size when compared across selected non-primate mammalian species (Mullender et al., 1996). Bromage et al. (2009: 393) reported an average of 58 148/mm3 osteocytes in three females of Rattus norvegicus that had an average body weight of 300 g. In contrast, a hippopotamus (Hippopotamus amphibius) with a body weight of 2000 kg exhibited 16 667/mm3 osteocytes (Bromage et al., 2009: 393). Furthermore, experimental findings suggest a relationship whereby bone and energy homeostasis is regulated through hormones that are involved in both bone cell biology and body mass accrual (Hogg et al., 2017; see their fig. 11.1). Taken together, these studies suggest a strong interspecific relationship between Ot.Dn and body size in mammals. This relationship, therefore, offers a unique way to investigate the growth of fossil rats from island settings. Hypothesis and prediction The goal of this study was to evaluate the island rule using Timorese fossil murine rodents whose body size would have ranged from small to giant, as inferred from their bone size. We studied osteocyte lacunae preserved in femoral midshaft samples to determine whether bone metabolic activity, indicative of tissue growth and related to life history, is related to body size among insular members of the rodent subfamily Murinae. We predicted that larger-bodied fossil specimens would have a slower rate of osteocyte proliferation in comparison to those with a smaller body. MATERIALS AND METHODS Samples We examined specimens that represent multiple species in the rodent subfamily Murinae from naturally accumulated Late Quaternary fossil deposits of Matja Kuru TD on Timor Island. Timor Island is located in eastern Wallacea, a region composed of > 17 000 islands. Having never been connected to Southeast Asia (SEA) or Australia, these islands represent permanently isolated geographical regions. Fossil material from this assemblage dates to a minimum of ~5000–18 000 years ago (Louys et al., 2017). It was impossible to identify the murine species positively from postcranial elements, meaning that we could not assign them to species or genus. Murine fossil material from Timor includes representatives of four giant extinct genera, of which only Coryphomys has been described formally, with two species currently recognized, Coryphomys buehleri and Coryphomys musseri (Schaub, 1937; Aplin & Helgen, 2010). We have no way of estimating the potential sex of our specimens; therefore, we cannot exclude sexual dimorphism as a confounding factor in our analyses. However, we note that previous research indicates it to be insignificant in small mammals (e.g. Lu et al., 2014). Giant murines have been on the island since at least the Middle Pleistocene (Louys et al., 2017) and are likely to have constituted part of the human diet until their extinction (Glover, 1971). The ten specimens represented nine right femora and one left femur (Fig. 1). The specimens and associated thin sections are housed at the Department of Archaeology and Natural History and the School of Archaeology and Anthropology at the Australian National University (Canberra, ACT, Australia; for accession numbers, see Tables 1 and 2). For consistency of sampling, the femora were selected based on preservation, side, midshaft completeness for thin sectioning, and ensuring that the final sample reflected a range of sizes. Bone histology and midshaft measurements for two of the specimens (TDS0-30#4 and TDD1#11) have been reported previously (Miszkiewicz et al., 2019). Most specimens were considered adult, as indicated by epiphyseal fusion and mature femoral form. However, some distal and proximal femoral ends were fragmented. We also acknowledge that epiphyseal plate fusion in mammals cannot be relied on entirely for age estimation (Geiger et al., 2014). Therefore, we supplemented the age estimates from bone morphology with identification of adult tissue in bone microscopic organization. For the small specimens, bone histology was very similar to that of adult Wistar rat (Rattus norvegicus) femoral cortex (see Singh & Gunberg, 1971; Martiniaková et al., 2005; Sengupta, 2013; Miszkiewicz et al., 2019). One of the giant femora (TDS0-30#4) also showed evidence of adult Haversian tissue (Miszkiewicz et al., 2019). Table 1. Raw data for the entire sample, reporting histology and gross morphometric femoral measurements in this study Femur accession ID (Australian National University) . MAXL . FHDM . MLW . CCD . Ot.N (a) . Section area (b) . Ot.Dn (a/b) . TDD 1 #1 n/a n/a 7.18 5.24 2778 0.929 2990.31 TDD 1 #2 n/a n/a 6.84 5.39 2380 0.927 2567.42 TDD 1 #3 n/a n/a 7.25 5.89 2292 0.923 2483.21 TDS 0–30 #4 n/a n/a 6.15* 4.87* 2569 0.844 3043.84 TDS 15–30 #6 n/a n/a 3.59 2.31 877 0.346 2534.68 TDD 1 #7 n/a n/a 4.18 3.02 1628 0.580 2806.90 TDD 1 #8 26.27 2.41 3.21 2.61 1218 0.375 3248.00 TDD 1 #9 29.73 3.51 3.85 2.5 1996 0.586 3406.14 TDD 1 #10 26.13 2.78 3.13 2.57 2287 0.581 3936.32 TDD 1 #11 n/a n/a 2.33* 1.98* 1579 0.452 3493.36 Femur accession ID (Australian National University) . MAXL . FHDM . MLW . CCD . Ot.N (a) . Section area (b) . Ot.Dn (a/b) . TDD 1 #1 n/a n/a 7.18 5.24 2778 0.929 2990.31 TDD 1 #2 n/a n/a 6.84 5.39 2380 0.927 2567.42 TDD 1 #3 n/a n/a 7.25 5.89 2292 0.923 2483.21 TDS 0–30 #4 n/a n/a 6.15* 4.87* 2569 0.844 3043.84 TDS 15–30 #6 n/a n/a 3.59 2.31 877 0.346 2534.68 TDD 1 #7 n/a n/a 4.18 3.02 1628 0.580 2806.90 TDD 1 #8 26.27 2.41 3.21 2.61 1218 0.375 3248.00 TDD 1 #9 29.73 3.51 3.85 2.5 1996 0.586 3406.14 TDD 1 #10 26.13 2.78 3.13 2.57 2287 0.581 3936.32 TDD 1 #11 n/a n/a 2.33* 1.98* 1579 0.452 3493.36 Abbreviations: CCD, cranial–caudal midshaft femoral depth (in millimetres); FHDM, femoral head diameter (in millimetres); MAXL, maximal intact femoral length (in millimetres); MLW, medial–lateral midshaft femoral width (in millimetres); n/a, not applicable; Ot.Dn, osteocyte lacunae number (a) divided by section area (b) (in millimetres squared); Ot.N (a), osteocyte lacunae number. *Data from Miszkiewicz et al. (2019). Open in new tab Table 1. Raw data for the entire sample, reporting histology and gross morphometric femoral measurements in this study Femur accession ID (Australian National University) . MAXL . FHDM . MLW . CCD . Ot.N (a) . Section area (b) . Ot.Dn (a/b) . TDD 1 #1 n/a n/a 7.18 5.24 2778 0.929 2990.31 TDD 1 #2 n/a n/a 6.84 5.39 2380 0.927 2567.42 TDD 1 #3 n/a n/a 7.25 5.89 2292 0.923 2483.21 TDS 0–30 #4 n/a n/a 6.15* 4.87* 2569 0.844 3043.84 TDS 15–30 #6 n/a n/a 3.59 2.31 877 0.346 2534.68 TDD 1 #7 n/a n/a 4.18 3.02 1628 0.580 2806.90 TDD 1 #8 26.27 2.41 3.21 2.61 1218 0.375 3248.00 TDD 1 #9 29.73 3.51 3.85 2.5 1996 0.586 3406.14 TDD 1 #10 26.13 2.78 3.13 2.57 2287 0.581 3936.32 TDD 1 #11 n/a n/a 2.33* 1.98* 1579 0.452 3493.36 Femur accession ID (Australian National University) . MAXL . FHDM . MLW . CCD . Ot.N (a) . Section area (b) . Ot.Dn (a/b) . TDD 1 #1 n/a n/a 7.18 5.24 2778 0.929 2990.31 TDD 1 #2 n/a n/a 6.84 5.39 2380 0.927 2567.42 TDD 1 #3 n/a n/a 7.25 5.89 2292 0.923 2483.21 TDS 0–30 #4 n/a n/a 6.15* 4.87* 2569 0.844 3043.84 TDS 15–30 #6 n/a n/a 3.59 2.31 877 0.346 2534.68 TDD 1 #7 n/a n/a 4.18 3.02 1628 0.580 2806.90 TDD 1 #8 26.27 2.41 3.21 2.61 1218 0.375 3248.00 TDD 1 #9 29.73 3.51 3.85 2.5 1996 0.586 3406.14 TDD 1 #10 26.13 2.78 3.13 2.57 2287 0.581 3936.32 TDD 1 #11 n/a n/a 2.33* 1.98* 1579 0.452 3493.36 Abbreviations: CCD, cranial–caudal midshaft femoral depth (in millimetres); FHDM, femoral head diameter (in millimetres); MAXL, maximal intact femoral length (in millimetres); MLW, medial–lateral midshaft femoral width (in millimetres); n/a, not applicable; Ot.Dn, osteocyte lacunae number (a) divided by section area (b) (in millimetres squared); Ot.N (a), osteocyte lacunae number. *Data from Miszkiewicz et al. (2019). Open in new tab Table 2. Raw data for individuals of 17 Asia-Pacific murine species of known weight and femoral midshaft size, along with the fossil specimens examined in the present study Taxon/ subfamily . Comparative significance . Institution . Accession number . Weight (g) . MLW (mm) . CCD (mm) . Parahydromys asper Asian native; waterside rat of New Guinea CSIRO #15689 470 4.99 3.47 Crossomys moncktoni Asian native; earless water rat of New Guinea CSIRO #15679, #15678 230 (240, 220) 3.90 (3.88, 3.92) 2.84 (2.85, 2.83) Pseudomys fumeus Southeast Australian native; smoky mouse of Australia CSIRO #13231 63 1.92 1.55 Conilurus penicillatus Australasian native; brush-tailed rabbit rat of Australia CSIRO #1007, #1009 107 (111, 103) 3.02 (2.97, 3.06) 2.77 (2.83, 2.71) Abeomelomys sevia Asian native; highland brush mouse of Papua New Guinea CSIRO #15693, #15694 (59.5) 64, 55 2.105 (2.33, 1.88) 1.56 (1.57, 1.54) Xeromys myoides Australasian native; false water rat of Australia and Papua New Guinea CSIRO #10022 44.5 2.42 1.62 Hydromys habbema Asian native; mountain water rat of West Papua, Indonesia and Papua New Guinea CSIRO #15691 68 2.66 1.95 Mallomys istapantap Asian native; subalpine woolly rat of West Papua, Indonesia and Papua New Guinea CSIRO #15681 1200 8.49 5.8 Rattus fuscipes Australasian native; bush rat of Australia CSIRO #17928, #17927, #17922, #17201 126.75 (110, 150, 133, 114) 2.67 (2.65, 3.03, 2.71, 2.27) 2.13 (2.15, 2.28, 2.17, 1.91) Rattus lutreolus Australasian native; swamp rat of Australia CSIRO #6806 129 3.13 2.06 Mus domesticus House mouse; included as a domesticated small rodent reference CSIRO #8624, #18846, #19463 14 (10, 14, 18) 1.41 (1.4, 1.33, 1.5) 1.17 (1.18, 1.18, 1.14) Paramelomys levipes Asian native; long-nosed mosaic-tailed rat of Papua New Guinea CSIRO #15695 44 1.64 1.46 Pogonomys loriae Asian native; tree mouse of Australia, Indonesia and Papua New Guinea CSIRO #16516 62 2.19 1.95 Protochromys fellowsi Asian native; red-bellied mosaic-tailed rat of Papua New Guinea CSIRO #16504 98 2.73 1.85 Melomys burtoni Australasian native; grassland mosaic-tailed rat of Australia and Papua New Guinea CSIRO #3673 40 1.79 1.74 Mammelomys sp. Asian native; rodent genus endemic to New Guinea KMH #1893 116 2.72 2.6 Rattus praetor Asian native; large spiny rat of Papua New Guinea and the Solomon Islands KMH #1833 435 4.81 3.49 Murinae sp. Asian material used in this study ANU TDD 1 #1 1015 7.18 5.24 Murinae sp. ANU TDD 1 #2 990 6.84 5.39 Murinae sp. ANU TDD 1 #3 1188 7.25 5.89 Murinae sp. ANU TDS 0–30 #4 765 6.15* 4.87* Murinae sp. ANU TDS 15–30 #6 156 3.59 2.31 Murinae sp. ANU TDD 1 #7 262 4.18 3.02 Murinae sp. ANU TDD 1 #8 158 3.21 2.61 Murinae sp. ANU TDD 1 #9 187 3.85 2.5 Murinae sp. ANU TDD 1 #10 150 3.13 2.57 Murinae sp. ANU TDD 1 #11 75 2.33* 1.98* Taxon/ subfamily . Comparative significance . Institution . Accession number . Weight (g) . MLW (mm) . CCD (mm) . Parahydromys asper Asian native; waterside rat of New Guinea CSIRO #15689 470 4.99 3.47 Crossomys moncktoni Asian native; earless water rat of New Guinea CSIRO #15679, #15678 230 (240, 220) 3.90 (3.88, 3.92) 2.84 (2.85, 2.83) Pseudomys fumeus Southeast Australian native; smoky mouse of Australia CSIRO #13231 63 1.92 1.55 Conilurus penicillatus Australasian native; brush-tailed rabbit rat of Australia CSIRO #1007, #1009 107 (111, 103) 3.02 (2.97, 3.06) 2.77 (2.83, 2.71) Abeomelomys sevia Asian native; highland brush mouse of Papua New Guinea CSIRO #15693, #15694 (59.5) 64, 55 2.105 (2.33, 1.88) 1.56 (1.57, 1.54) Xeromys myoides Australasian native; false water rat of Australia and Papua New Guinea CSIRO #10022 44.5 2.42 1.62 Hydromys habbema Asian native; mountain water rat of West Papua, Indonesia and Papua New Guinea CSIRO #15691 68 2.66 1.95 Mallomys istapantap Asian native; subalpine woolly rat of West Papua, Indonesia and Papua New Guinea CSIRO #15681 1200 8.49 5.8 Rattus fuscipes Australasian native; bush rat of Australia CSIRO #17928, #17927, #17922, #17201 126.75 (110, 150, 133, 114) 2.67 (2.65, 3.03, 2.71, 2.27) 2.13 (2.15, 2.28, 2.17, 1.91) Rattus lutreolus Australasian native; swamp rat of Australia CSIRO #6806 129 3.13 2.06 Mus domesticus House mouse; included as a domesticated small rodent reference CSIRO #8624, #18846, #19463 14 (10, 14, 18) 1.41 (1.4, 1.33, 1.5) 1.17 (1.18, 1.18, 1.14) Paramelomys levipes Asian native; long-nosed mosaic-tailed rat of Papua New Guinea CSIRO #15695 44 1.64 1.46 Pogonomys loriae Asian native; tree mouse of Australia, Indonesia and Papua New Guinea CSIRO #16516 62 2.19 1.95 Protochromys fellowsi Asian native; red-bellied mosaic-tailed rat of Papua New Guinea CSIRO #16504 98 2.73 1.85 Melomys burtoni Australasian native; grassland mosaic-tailed rat of Australia and Papua New Guinea CSIRO #3673 40 1.79 1.74 Mammelomys sp. Asian native; rodent genus endemic to New Guinea KMH #1893 116 2.72 2.6 Rattus praetor Asian native; large spiny rat of Papua New Guinea and the Solomon Islands KMH #1833 435 4.81 3.49 Murinae sp. Asian material used in this study ANU TDD 1 #1 1015 7.18 5.24 Murinae sp. ANU TDD 1 #2 990 6.84 5.39 Murinae sp. ANU TDD 1 #3 1188 7.25 5.89 Murinae sp. ANU TDS 0–30 #4 765 6.15* 4.87* Murinae sp. ANU TDS 15–30 #6 156 3.59 2.31 Murinae sp. ANU TDD 1 #7 262 4.18 3.02 Murinae sp. ANU TDD 1 #8 158 3.21 2.61 Murinae sp. ANU TDD 1 #9 187 3.85 2.5 Murinae sp. ANU TDD 1 #10 150 3.13 2.57 Murinae sp. ANU TDD 1 #11 75 2.33* 1.98* The specimens were studied by Ken Aplin and are registered at the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia). KMH refers to field number identifications, and the two KMH specimens are reposited at Bogor Zoology Museum (Bogor, Indonesia). We use these data for illustrative purposes only (see Fig. 1). Where data were collected for more than one individual per species, the weight, medial–lateral midshaft femoral width (MLW) and cranial–caudal midshaft femoral depth (CCD) are means. The estimated body mass for the Timor fossil murines is based on a phylogenetic generalized least squares regression, accounting for uncertainty in phylogenetic relationships and divergence times reported in text. *Data from Miszkiewicz et al. (2019). Open in new tab Table 2. Raw data for individuals of 17 Asia-Pacific murine species of known weight and femoral midshaft size, along with the fossil specimens examined in the present study Taxon/ subfamily . Comparative significance . Institution . Accession number . Weight (g) . MLW (mm) . CCD (mm) . Parahydromys asper Asian native; waterside rat of New Guinea CSIRO #15689 470 4.99 3.47 Crossomys moncktoni Asian native; earless water rat of New Guinea CSIRO #15679, #15678 230 (240, 220) 3.90 (3.88, 3.92) 2.84 (2.85, 2.83) Pseudomys fumeus Southeast Australian native; smoky mouse of Australia CSIRO #13231 63 1.92 1.55 Conilurus penicillatus Australasian native; brush-tailed rabbit rat of Australia CSIRO #1007, #1009 107 (111, 103) 3.02 (2.97, 3.06) 2.77 (2.83, 2.71) Abeomelomys sevia Asian native; highland brush mouse of Papua New Guinea CSIRO #15693, #15694 (59.5) 64, 55 2.105 (2.33, 1.88) 1.56 (1.57, 1.54) Xeromys myoides Australasian native; false water rat of Australia and Papua New Guinea CSIRO #10022 44.5 2.42 1.62 Hydromys habbema Asian native; mountain water rat of West Papua, Indonesia and Papua New Guinea CSIRO #15691 68 2.66 1.95 Mallomys istapantap Asian native; subalpine woolly rat of West Papua, Indonesia and Papua New Guinea CSIRO #15681 1200 8.49 5.8 Rattus fuscipes Australasian native; bush rat of Australia CSIRO #17928, #17927, #17922, #17201 126.75 (110, 150, 133, 114) 2.67 (2.65, 3.03, 2.71, 2.27) 2.13 (2.15, 2.28, 2.17, 1.91) Rattus lutreolus Australasian native; swamp rat of Australia CSIRO #6806 129 3.13 2.06 Mus domesticus House mouse; included as a domesticated small rodent reference CSIRO #8624, #18846, #19463 14 (10, 14, 18) 1.41 (1.4, 1.33, 1.5) 1.17 (1.18, 1.18, 1.14) Paramelomys levipes Asian native; long-nosed mosaic-tailed rat of Papua New Guinea CSIRO #15695 44 1.64 1.46 Pogonomys loriae Asian native; tree mouse of Australia, Indonesia and Papua New Guinea CSIRO #16516 62 2.19 1.95 Protochromys fellowsi Asian native; red-bellied mosaic-tailed rat of Papua New Guinea CSIRO #16504 98 2.73 1.85 Melomys burtoni Australasian native; grassland mosaic-tailed rat of Australia and Papua New Guinea CSIRO #3673 40 1.79 1.74 Mammelomys sp. Asian native; rodent genus endemic to New Guinea KMH #1893 116 2.72 2.6 Rattus praetor Asian native; large spiny rat of Papua New Guinea and the Solomon Islands KMH #1833 435 4.81 3.49 Murinae sp. Asian material used in this study ANU TDD 1 #1 1015 7.18 5.24 Murinae sp. ANU TDD 1 #2 990 6.84 5.39 Murinae sp. ANU TDD 1 #3 1188 7.25 5.89 Murinae sp. ANU TDS 0–30 #4 765 6.15* 4.87* Murinae sp. ANU TDS 15–30 #6 156 3.59 2.31 Murinae sp. ANU TDD 1 #7 262 4.18 3.02 Murinae sp. ANU TDD 1 #8 158 3.21 2.61 Murinae sp. ANU TDD 1 #9 187 3.85 2.5 Murinae sp. ANU TDD 1 #10 150 3.13 2.57 Murinae sp. ANU TDD 1 #11 75 2.33* 1.98* Taxon/ subfamily . Comparative significance . Institution . Accession number . Weight (g) . MLW (mm) . CCD (mm) . Parahydromys asper Asian native; waterside rat of New Guinea CSIRO #15689 470 4.99 3.47 Crossomys moncktoni Asian native; earless water rat of New Guinea CSIRO #15679, #15678 230 (240, 220) 3.90 (3.88, 3.92) 2.84 (2.85, 2.83) Pseudomys fumeus Southeast Australian native; smoky mouse of Australia CSIRO #13231 63 1.92 1.55 Conilurus penicillatus Australasian native; brush-tailed rabbit rat of Australia CSIRO #1007, #1009 107 (111, 103) 3.02 (2.97, 3.06) 2.77 (2.83, 2.71) Abeomelomys sevia Asian native; highland brush mouse of Papua New Guinea CSIRO #15693, #15694 (59.5) 64, 55 2.105 (2.33, 1.88) 1.56 (1.57, 1.54) Xeromys myoides Australasian native; false water rat of Australia and Papua New Guinea CSIRO #10022 44.5 2.42 1.62 Hydromys habbema Asian native; mountain water rat of West Papua, Indonesia and Papua New Guinea CSIRO #15691 68 2.66 1.95 Mallomys istapantap Asian native; subalpine woolly rat of West Papua, Indonesia and Papua New Guinea CSIRO #15681 1200 8.49 5.8 Rattus fuscipes Australasian native; bush rat of Australia CSIRO #17928, #17927, #17922, #17201 126.75 (110, 150, 133, 114) 2.67 (2.65, 3.03, 2.71, 2.27) 2.13 (2.15, 2.28, 2.17, 1.91) Rattus lutreolus Australasian native; swamp rat of Australia CSIRO #6806 129 3.13 2.06 Mus domesticus House mouse; included as a domesticated small rodent reference CSIRO #8624, #18846, #19463 14 (10, 14, 18) 1.41 (1.4, 1.33, 1.5) 1.17 (1.18, 1.18, 1.14) Paramelomys levipes Asian native; long-nosed mosaic-tailed rat of Papua New Guinea CSIRO #15695 44 1.64 1.46 Pogonomys loriae Asian native; tree mouse of Australia, Indonesia and Papua New Guinea CSIRO #16516 62 2.19 1.95 Protochromys fellowsi Asian native; red-bellied mosaic-tailed rat of Papua New Guinea CSIRO #16504 98 2.73 1.85 Melomys burtoni Australasian native; grassland mosaic-tailed rat of Australia and Papua New Guinea CSIRO #3673 40 1.79 1.74 Mammelomys sp. Asian native; rodent genus endemic to New Guinea KMH #1893 116 2.72 2.6 Rattus praetor Asian native; large spiny rat of Papua New Guinea and the Solomon Islands KMH #1833 435 4.81 3.49 Murinae sp. Asian material used in this study ANU TDD 1 #1 1015 7.18 5.24 Murinae sp. ANU TDD 1 #2 990 6.84 5.39 Murinae sp. ANU TDD 1 #3 1188 7.25 5.89 Murinae sp. ANU TDS 0–30 #4 765 6.15* 4.87* Murinae sp. ANU TDS 15–30 #6 156 3.59 2.31 Murinae sp. ANU TDD 1 #7 262 4.18 3.02 Murinae sp. ANU TDD 1 #8 158 3.21 2.61 Murinae sp. ANU TDD 1 #9 187 3.85 2.5 Murinae sp. ANU TDD 1 #10 150 3.13 2.57 Murinae sp. ANU TDD 1 #11 75 2.33* 1.98* The specimens were studied by Ken Aplin and are registered at the Commonwealth Scientific and Industrial Research Organisation (CSIRO, Australia). KMH refers to field number identifications, and the two KMH specimens are reposited at Bogor Zoology Museum (Bogor, Indonesia). We use these data for illustrative purposes only (see Fig. 1). Where data were collected for more than one individual per species, the weight, medial–lateral midshaft femoral width (MLW) and cranial–caudal midshaft femoral depth (CCD) are means. The estimated body mass for the Timor fossil murines is based on a phylogenetic generalized least squares regression, accounting for uncertainty in phylogenetic relationships and divergence times reported in text. *Data from Miszkiewicz et al. (2019). Open in new tab Figure 1. Open in new tabDownload slide The specimens examined in the present study (all in caudal view), showing the size gradient in the sample and midshaft sampling location (dashed line in A), a histological cross-section through one of the specimens and an associated region of interest examined for osteocyte lacunae (B), and examples of more (left) and less (right) widely dispersed osteocyte lacunae in a giant and small femur, respectively (C). Figure 1. Open in new tabDownload slide The specimens examined in the present study (all in caudal view), showing the size gradient in the sample and midshaft sampling location (dashed line in A), a histological cross-section through one of the specimens and an associated region of interest examined for osteocyte lacunae (B), and examples of more (left) and less (right) widely dispersed osteocyte lacunae in a giant and small femur, respectively (C). Femoral measurements We described the size of each femur quantitatively and compared them with a series of Asia-Pacific rodent species of known weight (Table 2). Two variables could be applied consistently across the specimens: femur midshaft width in a medial–lateral plane (MLW) and femur midshaft depth in a cranial–caudal (CCD) plane (in millimetres). These measurements were taken using standard digital callipers (Mitutoyo). The midshaft was either identified by dividing the length of intact femora in half or by locating shaft segments immediately distal to the third trochanter (dashed line in Fig. 1A). We report the maximal length and femoral head diameter where possible (Table 1), but exclude them from the statistical analyses because they represent only a fraction of our sample size. We computed body mass estimates using the femoral midshaft measurements. Given that this assemblage was commingled and only isolated dental remains were uncovered, a confident match between postcranial and cranial elements per individual was not possible. In addition to the fragmentation of the femora, this meant that we were unable to apply published body mass estimation methods, because they include dental data or they do not consider midshaft diameters only as proxies (e.g. Moncunill-Solé et al., 2014). Furthermore, given that our material was of SEA origin, it warranted the calculation of new, region-specific body mass regression equations based on our new data. Preparation of thin sections and imaging of bone histology Standard histological methods for fossil bone were followed to produce thin sections from each femoral midshaft (Chinsamy & Raath, 1992; Miszkiewicz et al., 2019). Femora were embedded in Buehler epoxy resin and cut at midshaft in a transverse plane using a Kemet MICRACUT 151 Precision Cutter with a diamond cutting blade. Samples were then glued to microscope slides using Araldite, ground and polished on a series of pads and cloths, dehydrated in ethanol (95 and 100%) baths, cleared in xylene, and coverslipped using a DPX mounting medium. The resulting sections were ~100–150 μm thick. Micro-anatomical descriptions indicate that rat compact bone is mostly avascular, marked with radial canals, with osteocytes residing within osteocyte lacunae (Martiniaková et al., 2005; Oršolić et al., 2018). Haversian, remodelled tissue in murine bone has been reported in only a few case studies (Kolb et al., 2015; Miszkiewicz et al., 2019). Given that osteocytes are responsible for bone maintenance, they essentially sustain living tissue by signalling mechanical load and facilitating the exchange of nutrients (Han et al., 2004; Knothe Tate et al., 2004). Osteocytes are the most abundant bone cell found in vertebrates (Hall, 2015), and although the cells themselves are not typically preserved in fossil bone, the cavities in which they would have resided can preserve. Osteocyte lacunae in fossil or archaeological bone can thus be studied as a proxy for osteocyte proliferation and bone metabolism (Bromage et al., 2009; Miszkiewicz, 2016; Hogg et al., 2017; Miszkiewicz & Mahoney, 2017). We accessed these micro-features from each thin section using standard light microscopy (Olympus BX51 or BX53 microscope with a DP73 or DP74 camera, respectively) and analysed them in ImageJ (1.51k, 2013). All sections were first imaged at a ×40 total magnification (~6.07 mm2 each image) in order to produce an overview micrograph for each sample. For the larger femoral sections, an average of ten to 14 individual images were collected, whereas the smaller femora were easily reproduced from two to three individual images. Each of these was stitched manually in Adobe Photoshop CC 2014 to create a starting point from which to identify the best-preserved and taphonomy/bio-erosion-free region of interest. Unlike modern or fresh bone, the palaeontological context of our samples meant that there was incomplete and inconsistent preservation of the microstructure. Therefore, the selection of regions of interest for data collection was determined by the visibility of, and our confidence in identifying, osteocyte lacunae. Where possible, we selected the same anatomical aspect of each femur, in order that osteocyte lacunae data could be compared consistently across the whole sample. This resulted in isolation of the lateral femur region, with some caudal or cranial overlap (Fig. 1B). Ultimately, we captured osteocyte lacunae data from one region of interest per section at ×100 total magnification, representing an image that measures ~0.93 mm2. The bone area within each image ranged from ~0.93 mm2 in the giant rats to ~0.35 mm2 in the smaller rats. In the latter case, the area of the bone itself was measured by directly tracing the bone tissue, excluding regions of the image that were empty. Using the MultiPoint tool in ImageJ (1.51k, 2013), osteocyte lacunae were first recorded as total counts from the most superior surface of each section. Before counting, all images were adjusted to grey scale (black and white intensity = 100) and then exposed (offset = −0.100) in Adobe Photoshop CC 2014 to enhance each lacuna so that they could be distinguished against the white background (Fig. 1B). In order to estimate densities, a standard Ot.Dn (osteocyte lacunae density = osteocyte lacunae count/section area in millimetres squared) variable was created by dividing each osteocyte lacunae count by the bone area examined (in millimetres squared; Li et al., 2011; Miszkiewicz, 2016). To check for potential observer bias, osteocyte lacunae in two randomly selected images from our image bank were scored independently by three observers: two authors of the present study (J.J.M. and J.L.) and one external histologist (T. J. Stewart). Statistical analyses All statistical analyses were conducted in IBM SPSS Statistics v.22.0 (2013), Past3 (Hammer et al., 2001) and R v.3.6.0. We split the analyses into two steps: (1) testing for a phylogenetic signal and creating body mass regressions; and (2) assessing relationships between measures of body size and Ot.Dn by examining linear trends and testing for allometric changes (Kilmer & Rodríguez, 2017). Given that we had only three independent data points, inter-observer measurements were compared between the repeated data descriptively by assessing the extent of deviation from the mean. The measurements were deemed repeatable if the disagreement was < 5%. Phylogenetic signal and body mass regressions To produce a body mass regression equation that could be used to estimate body mass for our Timorese specimens, we collected CCD and MLW measurements for specimens of known body mass for 17 Asia-Pacific murine species (Table 2). Where data were available for multiple specimens of the same species, these were combined to produce mean estimates for that species (Table 2). The final CCD and MLW measurements and body mass for each species were natural logarithmically (ln) transformed before analysis. We used a phylogenetic generalized least squares (PGLS) approach (Symonds & Blomberg, 2014), with uncertainty in phylogenetic relationships and divergence times among our species taken into account using the R package sensiPhy (Paterno et al., 2018) and 1000 trees from the ‘Phylacine’ database (Faurby et al., 2018), pruned to match our set of 17 species using the ‘keep.tip’ function of the R package ape (Paradis & Schliep, 2019). We used the ‘physig’ function of sensiPhy to calculate the maximum likelihood estimate of Pagel’s λ in the residuals of our data as a measure of phylogenetic signal, and then used this value of λ to determine the best-fitting regression. We calculated three different regressions, using body mass and: CCD, MLW or the cross-sectional area of the femoral midshaft, which we calculated as π × (0.5 × CCD) × (0.5 × MLW), i.e. treating it as an ellipse. We then used the Akaike information criterion to determine which of these three regressions showed the best fit to our data, and used the best-fitting regression to estimate body mass for our Timorese specimens. Evaluating relationships between femur size, body mass and osteocyte lacunae Initially, all the raw data for body mass estimates (in grams), CCD (in millimetres), MLW (in millimetres) and Ot.Dn were correlated using non-parametric Spearman’s ρ tests to assess linear agreements between data. These were repeated on the raw Ot.Dn data corrected by femur midshaft size (Ot.Dn/MLW and Ot.Dn/CCD). The results from these correlations were interpreted following Taylor (1990), whereby ρ > 0.67 is considered a high or strong correlation. To assess allometric changes in Ot.Dn along with femur size and body mass estimates, we used ordinary least squares (OLS) regressions on log10-transformed data (which decreased data variability). We interpret the r2, slope (b), confidence interval (CI), intercept (Y) and statistical significance of these models using uncorrected P-values (α = 0.05) in addition to Bonferroni-corrected (uncorrected P-value divided by the number of repeated tests; α = 0.017), more conservative P-values for each set of analysis. Plots fitting OLS regressions illustrate the trend line and CIs to describe the scatter of data visually. RESULTS There was no inter-observer error in the independent measurements, with the three observers providing almost equal counts of lacunae per image (image 1, mean 127.33, SD 2.08, similarity = 98.37%; image 2, mean 132.67, SD = 2.52, similarity = 98.10%). The largest midshaft femur measured 7.25 mm in MLW and 5.89 mm in CCD (Tables 1–3). The smallest examined femur was of 2.33 mm in MLW and 1.98 mm in CCD (Miszkiewicz et al., 2019). Estimates of body mass for the sample ranged from 75 g for the smallest specimen to 1188 g for the largest specimen. We incorporated these estimates (relying on MLW and CCD data) into a bar chart encompassing modern data for rats of known weight (Fig. 2; Table 2). This showed that the smaller fossil murines were likely to be similar in their body mass to a house mouse (Mus domesticus), whereas the giant murids might have been comparable to a subalpine woolly rat (Mallomys istapantap, up to 2 kg in weight). Figure 2. Open in new tabDownload slide Estimated body weight (in grams; top panel) and midshaft femur measurements in medial–lateral and cranial–caudal planes (in millimetres; bottom panel) for the Timor specimens (indicated in the graph by the boxes) presented amongst 17 other Asia-Pacific murine rodents of known weight. Figure 2. Open in new tabDownload slide Estimated body weight (in grams; top panel) and midshaft femur measurements in medial–lateral and cranial–caudal planes (in millimetres; bottom panel) for the Timor specimens (indicated in the graph by the boxes) presented amongst 17 other Asia-Pacific murine rodents of known weight. Body mass estimates The phylogenetic signal (measured by Pagel’s λ) in our data for specimens of known body mass for 17 Asia-Pacific species (Table 2) was very low and non-significant (mean = 0.03, CI = 0.02–0.04, P = 0.99). Akaike information criterion values for our three regressions were as follows: ln(MLW femoral width) = 10.38, ln(CCD femoral depth) = 16.53 and ln(femoral midshaft cross-sectional area femur area) = 8.59. Given that lower values of the Akaike information criterion represent a better model fit, it is clear that combining femoral width and depth into an estimate of femoral area resulted in a better-fitting model. The PGLS regression for ln(femoral area) and the maximum likelihood estimate of Pagel’s λ (λ = 0.03) was: ln(body mass)=1.24×ln[femur area=π×(0.5×CCD)×(0.5×MLW)]+2.724 Estimates of body mass for the Timor specimens, based on the above equation, are reported in Table 2. Osteocyte lacunae densities The density of osteocyte lacunae ranged from 2483.21 to 3936.32/mm2. However, corrections by femoral size adjusted the data to a range from 342.51 to 1499.30/mm2 in the MLW category and from 421.60 to 1764.32/mm2 in the CCD measure of the femoral shaft (Tables 1–3). The results of Spearman’s ρ tests (Table 4) suggested that Ot.Dn data were in strongly negative and statistically significant relationships with measures of femoral size and estimates of body mass. The ρ achieved in these cases was −0.952 to −0.661, with P < 0.05. However, when CCD was considered, these relationships were not consistent, whereby ρ was −0.576 (P = 0.082) when raw Ot.Dn were included in the analysis. When using a more conservative Bonferroni correction on repeated tests, the correlation between estimated body mass and MLW or raw Ot.Dn did not achieve significance, with P = 0.038 and 0.019, respectively. Table 3. Data for the entire murine sample representing femoral morphometric and histological measurements Variable . N . Minimum . Maximum . Mean . SD . MAXL 3 26.13 29.73 27.38 2.04 FHDM 3 2.41 3.51 2.90 0.56 MLW 10 2.33 7.25 4.77 1.88 CCD 10 1.98 5.89 3.64 1.51 Ot.N (#) 10 877.00 2778.00 1960.40 615.87 Section area (mm2) 10 0.35 0.93 0.65 0.23 Ot.Dn (#/mm2) 10 2483.21 3936.32 3051.02 474.99 Ot.Dn/MLW 10 342.51 1499.30 766.03 393.57 Ot.Dn/CCD 10 421.60 1764.32 1002.32 472.12 Variable . N . Minimum . Maximum . Mean . SD . MAXL 3 26.13 29.73 27.38 2.04 FHDM 3 2.41 3.51 2.90 0.56 MLW 10 2.33 7.25 4.77 1.88 CCD 10 1.98 5.89 3.64 1.51 Ot.N (#) 10 877.00 2778.00 1960.40 615.87 Section area (mm2) 10 0.35 0.93 0.65 0.23 Ot.Dn (#/mm2) 10 2483.21 3936.32 3051.02 474.99 Ot.Dn/MLW 10 342.51 1499.30 766.03 393.57 Ot.Dn/CCD 10 421.60 1764.32 1002.32 472.12 Measurements are as defined in Table 1. #Indicates number (count of osteocyte lacunae). Open in new tab Table 3. Data for the entire murine sample representing femoral morphometric and histological measurements Variable . N . Minimum . Maximum . Mean . SD . MAXL 3 26.13 29.73 27.38 2.04 FHDM 3 2.41 3.51 2.90 0.56 MLW 10 2.33 7.25 4.77 1.88 CCD 10 1.98 5.89 3.64 1.51 Ot.N (#) 10 877.00 2778.00 1960.40 615.87 Section area (mm2) 10 0.35 0.93 0.65 0.23 Ot.Dn (#/mm2) 10 2483.21 3936.32 3051.02 474.99 Ot.Dn/MLW 10 342.51 1499.30 766.03 393.57 Ot.Dn/CCD 10 421.60 1764.32 1002.32 472.12 Variable . N . Minimum . Maximum . Mean . SD . MAXL 3 26.13 29.73 27.38 2.04 FHDM 3 2.41 3.51 2.90 0.56 MLW 10 2.33 7.25 4.77 1.88 CCD 10 1.98 5.89 3.64 1.51 Ot.N (#) 10 877.00 2778.00 1960.40 615.87 Section area (mm2) 10 0.35 0.93 0.65 0.23 Ot.Dn (#/mm2) 10 2483.21 3936.32 3051.02 474.99 Ot.Dn/MLW 10 342.51 1499.30 766.03 393.57 Ot.Dn/CCD 10 421.60 1764.32 1002.32 472.12 Measurements are as defined in Table 1. #Indicates number (count of osteocyte lacunae). Open in new tab Table 4. Spearman’s ρ correlations and ordinary least squares regressions assessing relationships between osteocyte lacunae density (Ot.Dn) and rat femoral size or estimated body mass (using raw and log10-transformed data, respectively): coefficient of determination (r2), slope (b), confidence interval (CI), intercept (Y) x-axis . y-axis . ρ . P-value . Estimated body mass (g) Ot.Dn (#/mm2) −0.661 < 0.038* Ot.Dn/CCD −0.939 < 0.0001*† Ot.Dn/MLW −0.952 < 0.0001*† CCD (mm) Ot.Dn (#/mm2) −0.576 0.082 Ot.Dn/CCD −0.915 < 0.0001*† Ot.Dn/MLW −0.891 0.001*† MLW (mm) Ot.Dn (#/mm2) −0.721 0.019* Ot.Dn/CCD −0.952 < 0.0001*† Ot.Dn/MLW −0.976 < 0.0001*† OLS x-axis y-axis r2, b, Y, CI P-value Log estimated body mass Log Ot.Dn 0.367, −0.092, 8.546, −0.178 −0.024 0.064 Log Ot.Dn/CCD 0.952, −0.498, 9.678, −0.568 −0.429 < 0.0001*† Log Ot.Dn/MLW 0.917, −0.491, 9.361, −0.560 −0.418 < 0.0001*† Log CCD Log Ot.Dn 0.317, −0.210, 8.268, −0.415 −0.028 0.090 Log Ot.Dn/CCD 0.940, −1.211, 8.269, −1.436 −1.036 < 0.0001*† log Ot.Dn/MLW 0.864, −1.167, 7.94, −1.486 −0.920 < 0.0001*† Log MLW Log Ot.Dn 0.409, −0.243, 8.376, −0.436 −0.069 0.046* Log Ot.Dn/CCD 0.937, −1.233, 8.637, −1.432 −0.965 < 0.0001*† Log Ot.Dn/MLW 0.947, −1.244, 8.378, −1.432 −1.058 < 0.0001*† x-axis . y-axis . ρ . P-value . Estimated body mass (g) Ot.Dn (#/mm2) −0.661 < 0.038* Ot.Dn/CCD −0.939 < 0.0001*† Ot.Dn/MLW −0.952 < 0.0001*† CCD (mm) Ot.Dn (#/mm2) −0.576 0.082 Ot.Dn/CCD −0.915 < 0.0001*† Ot.Dn/MLW −0.891 0.001*† MLW (mm) Ot.Dn (#/mm2) −0.721 0.019* Ot.Dn/CCD −0.952 < 0.0001*† Ot.Dn/MLW −0.976 < 0.0001*† OLS x-axis y-axis r2, b, Y, CI P-value Log estimated body mass Log Ot.Dn 0.367, −0.092, 8.546, −0.178 −0.024 0.064 Log Ot.Dn/CCD 0.952, −0.498, 9.678, −0.568 −0.429 < 0.0001*† Log Ot.Dn/MLW 0.917, −0.491, 9.361, −0.560 −0.418 < 0.0001*† Log CCD Log Ot.Dn 0.317, −0.210, 8.268, −0.415 −0.028 0.090 Log Ot.Dn/CCD 0.940, −1.211, 8.269, −1.436 −1.036 < 0.0001*† log Ot.Dn/MLW 0.864, −1.167, 7.94, −1.486 −0.920 < 0.0001*† Log MLW Log Ot.Dn 0.409, −0.243, 8.376, −0.436 −0.069 0.046* Log Ot.Dn/CCD 0.937, −1.233, 8.637, −1.432 −0.965 < 0.0001*† Log Ot.Dn/MLW 0.947, −1.244, 8.378, −1.432 −1.058 < 0.0001*† Measurements are as defined in Table 1. The total sample size is ten in each test. #Indicates number (count of osteocyte lacunae). *Statistically significant at P < 0.05. †Statistically significant at Bonferroni-corrected P < 0.017. Open in new tab Table 4. Spearman’s ρ correlations and ordinary least squares regressions assessing relationships between osteocyte lacunae density (Ot.Dn) and rat femoral size or estimated body mass (using raw and log10-transformed data, respectively): coefficient of determination (r2), slope (b), confidence interval (CI), intercept (Y) x-axis . y-axis . ρ . P-value . Estimated body mass (g) Ot.Dn (#/mm2) −0.661 < 0.038* Ot.Dn/CCD −0.939 < 0.0001*† Ot.Dn/MLW −0.952 < 0.0001*† CCD (mm) Ot.Dn (#/mm2) −0.576 0.082 Ot.Dn/CCD −0.915 < 0.0001*† Ot.Dn/MLW −0.891 0.001*† MLW (mm) Ot.Dn (#/mm2) −0.721 0.019* Ot.Dn/CCD −0.952 < 0.0001*† Ot.Dn/MLW −0.976 < 0.0001*† OLS x-axis y-axis r2, b, Y, CI P-value Log estimated body mass Log Ot.Dn 0.367, −0.092, 8.546, −0.178 −0.024 0.064 Log Ot.Dn/CCD 0.952, −0.498, 9.678, −0.568 −0.429 < 0.0001*† Log Ot.Dn/MLW 0.917, −0.491, 9.361, −0.560 −0.418 < 0.0001*† Log CCD Log Ot.Dn 0.317, −0.210, 8.268, −0.415 −0.028 0.090 Log Ot.Dn/CCD 0.940, −1.211, 8.269, −1.436 −1.036 < 0.0001*† log Ot.Dn/MLW 0.864, −1.167, 7.94, −1.486 −0.920 < 0.0001*† Log MLW Log Ot.Dn 0.409, −0.243, 8.376, −0.436 −0.069 0.046* Log Ot.Dn/CCD 0.937, −1.233, 8.637, −1.432 −0.965 < 0.0001*† Log Ot.Dn/MLW 0.947, −1.244, 8.378, −1.432 −1.058 < 0.0001*† x-axis . y-axis . ρ . P-value . Estimated body mass (g) Ot.Dn (#/mm2) −0.661 < 0.038* Ot.Dn/CCD −0.939 < 0.0001*† Ot.Dn/MLW −0.952 < 0.0001*† CCD (mm) Ot.Dn (#/mm2) −0.576 0.082 Ot.Dn/CCD −0.915 < 0.0001*† Ot.Dn/MLW −0.891 0.001*† MLW (mm) Ot.Dn (#/mm2) −0.721 0.019* Ot.Dn/CCD −0.952 < 0.0001*† Ot.Dn/MLW −0.976 < 0.0001*† OLS x-axis y-axis r2, b, Y, CI P-value Log estimated body mass Log Ot.Dn 0.367, −0.092, 8.546, −0.178 −0.024 0.064 Log Ot.Dn/CCD 0.952, −0.498, 9.678, −0.568 −0.429 < 0.0001*† Log Ot.Dn/MLW 0.917, −0.491, 9.361, −0.560 −0.418 < 0.0001*† Log CCD Log Ot.Dn 0.317, −0.210, 8.268, −0.415 −0.028 0.090 Log Ot.Dn/CCD 0.940, −1.211, 8.269, −1.436 −1.036 < 0.0001*† log Ot.Dn/MLW 0.864, −1.167, 7.94, −1.486 −0.920 < 0.0001*† Log MLW Log Ot.Dn 0.409, −0.243, 8.376, −0.436 −0.069 0.046* Log Ot.Dn/CCD 0.937, −1.233, 8.637, −1.432 −0.965 < 0.0001*† Log Ot.Dn/MLW 0.947, −1.244, 8.378, −1.432 −1.058 < 0.0001*† Measurements are as defined in Table 1. The total sample size is ten in each test. #Indicates number (count of osteocyte lacunae). *Statistically significant at P < 0.05. †Statistically significant at Bonferroni-corrected P < 0.017. Open in new tab Ordinary least squares regression of all log10-transformed data resulted in an almost consistent statistical significance and strong models that showed negative allometry (Table 4). Two of the models, log(estimated body mass) and log(Ot.Dn), and log(CCD) and log(Ot.Dn), returned P > 0.05 and had weak r2. However, most of the models were statistically significant at α = 0.05, except for Bonferroni-corrected log(MLW) and log(Ot.Dn), for which 0.05 > P > 0.017. The data scatter around regression lines was wider in the cases where raw data were used, but better-fitting models can be seen for those where the size of the femur is accounted for in Ot.Dn (Fig. 3). Figure 3. Open in new tabDownload slide Negative allometric relationships between the logarithm of estimated body mass (top row), the logarithm of cranial–caudal (middle row) and the logarithm of medial–lateral midshaft (bottom row) diameter data (CCD and MLW respectively), and the logarithm of osteocyte lacunae (Ot.Dn) (including data corrected by midshaft size, y-axis) in the sample. The regression line is red, and the confidence interval is indicated by blue lines. Figure 3. Open in new tabDownload slide Negative allometric relationships between the logarithm of estimated body mass (top row), the logarithm of cranial–caudal (middle row) and the logarithm of medial–lateral midshaft (bottom row) diameter data (CCD and MLW respectively), and the logarithm of osteocyte lacunae (Ot.Dn) (including data corrected by midshaft size, y-axis) in the sample. The regression line is red, and the confidence interval is indicated by blue lines. Discussion Our analyses revealed statistically significant negative correlations, and an allometric relationship between the histological and macroscopic measures of bone metabolism and body mass, in a range of giant and small fossil murine rodents from Timor Island. Collectively, these results provide clear evidence that fossil murine gigantism was associated with a slowing down of bone metabolism, as inferred from low osteocyte lacunae densities. In contrast, the smaller murines in our sample exhibited higher osteocyte lacunae densities, indicating accelerated bone metabolism. Our study has implications for our current understanding of the evolution of mammalian bone physiology in relationship to body mass and insularity, in addition to the palaeoenvironments of Timor. Bone metabolism This study unlocks bone physiology from cell structures preserved in thin sections of fossil femora, enabling us to understand the biological adaptation of Timorese island members of the rodent subfamily Murinae and to examine the relationships between bone osteocyte lacunae densities and body mass. We have previously shown that changes in osteocyte lacunae densities can be linked to bone remodelling rates (e.g. Miszkiewicz, 2016) and, as such, can provide insights into fluctuations in bone metabolism. When examined within living mammals, strong inverse correlations between Ot.Dn and body mass show that osteocyte proliferation corresponds to body mass (Hogg et al., 2017). Data presented here support these ideas, because they demonstrate a strong decline in Ot.Dn with increasing body mass within Timorese island murines. These data are similar to previous interspecific findings for extant non-primate mammals (Mullender et al., 1996) and to those described by Bromage et al. (2009: 393) for species that included adult pygmy (Phanourios minutus) and common hippopotami (Hippopotamus amphibious), the Mohol bushbaby (lesser galagos, Galago moholi) and the greater dwarf lemur (Cheirogales major). A pygmy hippopotamus of ~200 kg body mass had an average Ot.Dn reported as 23 641/mm3, whereas its larger counterpart (Hippopotamus amphibius) had an Ot.Dn of 16 667/mm3. In the same study, an adult lesser galago with a weight of ~244 g had an Ot.Dn of 51 724/mm3, which was much higher than the Ot.Dn of 31 526/mm3 from a greater galago with a body weight of 400 g. Our data conform to this general pattern. Our study shows much more widely dispersed osteocyte lacunae in the giant murine specimen when compared with its smaller counterpart (Fig. 1C), and the body size and Ot.Dn are related through negative allometry. Bone histology limitations of our study pertain to being understood in two dimensions only, whereas three-dimensional scans of each entire femur in the sample might yield more data on osteocyte lacunae in the future. We are also unable to make further connections to energy variables, such as the basal metabolic rate, because of the nature of the samples. With no direct measures of muscle or physical activity in our fossil murine sample, we are limited in understanding how their energetic expenditure and heat generation might have fitted into life-history strategies (McNab, 2019). Finally, the unknown species identification limited our interpretations of the links between Ot.Dn and phylogeny. However, previous accounts of interspecific variation in bone micro-organization have cited animal size and lifespan as more direct influences on histology than phylogeny (de Ricqlès, 1993; Greenlee & Dunnell, 2010). The extinct giant murines of Timor As predicted by the island rule, animals may change in response to insular environments owing to selective pressures that encourage anatomical and behavioural modifications. Smaller, lighter and faster-growing mammals can adapt more easily than those that have higher energetic demands. Although being smaller is associated with many advantages, it also decreases longevity, as outlined in the classical r- and K-selection evolutionary strategy principles (Pianka, 1970). The relatively slow bone metabolism of giant Timorese murines could indicate extended lifespans, which could be linked to favourable palaeoenvironments. It is extremely difficult to pinpoint the specific cause of extinction of our giant murines, because many factors must have played a role in their demise. However, when compared with prior palaeobiological models that test extinction causality in small mammals on islands (e.g. Bover & Alcover, 2008), we can at least propose some environmental extinction elements. For example, Bover & Alcover (2008) examined the extinction of Mallorcan small mammals by analysing climate, predation, competition, habitat loss/modification and anthropogenic factors as potential reasons driving extinction in the Western Mediterranean. The authors obtained radiocarbon ages from fossil bone collagen to reconstruct uncertainty and restricted periods of extinction for species of Balearic dormouse (Eliomys morpheus) and the Balearic shrew (Asoriculus hidalgo) and corroborated archaeological data and direct dating data of the introduced garden dormouse Eliomys quercinus and the wood mouse Apodemus sylvaticus. They concluded that the extinction of the Mallorcan small mammals would most probably have been caused indirectly by human activity (the spread of disease). For the giant murines of Timor, we can find supporting evidence in the historical and archaeological records for at least two of these items: human coexistence with giant murines and habitat modification on the island of Timor. Fossil evidence suggests that giant murines were in Timor from the Middle Pleistocene (Louys et al., 2017), by which time the island was also home to small-bodied stegodons (Stegodon ‘trigonocephalus’ and Stegodon timorensis), which were elephant-like animals that might have evolved into pygmy forms on the island (Louys et al., 2016). This hints at the effect of insularity impacting more than one mammal in Timor. To that end, giant murines have been found in association with humans in Timor for > 40 000 years (Hawkins et al., 2017). Glover (1971: 177), when reviewing archaeological and palaeontological excavations on the island of Timor since ~1935, noted that giant murines would have been ‘the principal prey’ (in addition to pteropodid bats) of the first human groups. Increasing human contact might not only have entailed predation, because it would also have been likely to lead to significant habitat alteration and the introduction of competitors, other predators and disease. Human-driven deforestation in SEA is a well-established issue that contributes to the reduction of resources and elimination of forest ecology (see McWilliam, 2005; O’Connor et al., 2012). Modern biodiversity conservation efforts have continually documented the disappearance of rich native habitats in areas densely populated and exploited by humans in SEA (Sodhi et al., 2010; Hughes, 2017; Carlson et al., 2018). Historical annotations indicate that Timor became an important centre for timber export of white sandalwood ~1500 AD (McWilliam, 2005; O’Connor et al., 2012), with prior introduction of metal tools (bronze and iron) to island SEA sometime between 2500 and 1500 years ago (Higham, 1996; Bulbeck, 2008). These tool developments would have facilitated effective slash-and-burn agriculture, with the later timber export activity accelerating forest cultivation. By the Timorese fort-building period, ~1500 years ago, many small but no giant murine fossils are recovered in excavations, suggesting extinction of the latter by this time (O’Connor & Aplin, 2007). Although more direct evidence for the Timor palaeoenvironments and a larger sample size are needed, our histological study suggests that the slower bone metabolism of giant murids fitted the principles of gigantism under the island rule. These murids might have experienced slow growth and maturation, requiring relatively larger amounts of energy obtained from good-quality or a large quantity of resources, with low levels of predation, all of which facilitated longevity and increased the quality of offspring (Reznick et al., 2002; Dammhahn et al., 2018). Our findings match those from another palaeohistological study that inferred an ‘exceptionally long lifespan’ (Orlandi-Oliveras et al., 2016: 238) based on bone histology of a giant fossil glirid rodent, Hypnomys onicensis, on the Balearic Islands, confirming the slower life history in an insular context. We acknowledge that true ‘gigantism’ of our specimens cannot be confirmed until we know the body mass of their ancestors and have an accurate phylogeny. The island rule specifies that if a colonizing ancestral species was initially small and the newly colonized island marked with favourable habitats, evolution into a giant form would be selectively advantageous. Although we know that Timor has never been connected to SEA or Australia, and thus has been truly isolated geographically throughout its history, cases of island rodents that have evolved into dwarfed forms from larger forms after deterioration in food resources are known (Durst & Roth, 2015). Conclusions Laboratory rats have long been used in biology research, allowing us to observe phenotypic changes in these animals upon experimental modification of external environmental and internal genotypic conditions. Here, we conducted an experiment in deep time, assessing the size and bone microanatomy of murines in the context of a changing and insular environment. The gradient of murine size in this sample served as a platform for investigating links between bone metabolism and its response to insularity. We showed that the now extinct giant murines of Timor were likely to be characterized by slow bone metabolism, which could be related to abundant resources and plentiful forests until human-driven action destroyed these habitats. This finding is consistent with predictions made from the island rule. We also found that surviving smaller murines were equipped with faster bone metabolism, allowing them to survive less certain environmental contexts once anthropogenic alteration increased. These findings further our understanding of vertebrate bone tissue metabolism, its adaptation in response to ecological change, along with its versatility and plasticity that can be reconstructed at a microscopic level. ACKNOWLEDGEMENTS Shimona Kealy selected murine specimens for this experiment. Tahlia Stewart participated in the inter-observer error test. 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OpenURL Placeholder Text WorldCat Author notes Deceased. © 2020 The Linnean Society of London, Biological Journal of the Linnean Society This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)
TI - Island rule and bone metabolism in fossil murines from Timor
JF - Biological Journal of the Linnean Society
DO - 10.1093/biolinnean/blz197
DA - 2020-02-28
UR - https://www.deepdyve.com/lp/oxford-university-press/island-rule-and-bone-metabolism-in-fossil-murines-from-timor-3wxKK6sDVp
SP - 570
VL - 129
IS - 3
DP - DeepDyve
ER -