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- Materials and methods
- Supporting Information
Natal dispersal, the movement an animal, plant or propagule undertakes from its point of origin to the place where it reproduces, is a demographic parameter which is almost ubiquitous in its importance to ecology and evolution (Howard 1960; Greenwood 1980; Vellend & Orrock 2010). Its effects are seen at all spatial scales, from the distribution of genes and individuals within populations to community composition and the position and extent of species geographic ranges (Slatkin 1987; Johnson & Gaines 1990; Stenseth & Lidicker 1992; Gaston 2003; Dytham 2009). In addition, there is now a growing understanding about the importance of dispersal in enabling species to keep pace with changing climates (Anderson et al. 2012; Urban, Tewksbury & Sheldon 2012). A recent framework, constructed to identify the threats and benefits of climate change for individual species, highlighted dispersal as an exacerbating factor important in identifying species at risk (Thomas et al. 2010). There is therefore a burgeoning need to include quantitative descriptions of dispersal in spatially explicit population models and species distribution modelling (SDM) techniques (Araújo & Guisan 2006; Hawkes 2009).
The ability to accurately parameterize dispersal distance within models is severely hampered in many taxonomic groups by a lack of data (Nathan 2001). For example, SDMs predicting the impact of climate change on species distributions frequently contrast scenarios of unconstrained and no dispersal with the caveat that, in reality, most species will show a range of dispersal distances which fall between these two assumptions (Araújo & Rahbek 2006; Broennimann et al. 2006; Botkin et al. 2007). There are a number of reasons for this shortfall, including inconsistencies in measurements and definitions of dispersal, difficulties in collecting empirical data owing to the ‘once in a lifetime’ nature of dispersal events in many species and the inability of most studies to capture long-distance dispersal (LDD) events (Koenig, VanVuren & Hooge 1996; Bowman, Jaeger & Fahrig 2002). Difficulties also arise because the process itself takes many forms, from a gradual range shift to an adjacent home range, to a one-way movement over a great distance (Stenseth & Lidicker 1992).
Identifying significant correlations between dispersal distance and biological or ecological traits may also help us to estimate dispersal ability when data are scarce. Linear relationships have been established between dispersal distance and body mass (Van Vuren 1998; Sutherland et al. 2000; Jenkins et al. 2007) and dispersal distance and home range size (Bowman, Jaeger & Fahrig 2002; Ottaviani et al. 2006) for selected subsets of mammals and birds. There is good evidence linking life-history and demographic traits to dispersal distance in a variety of invertebrate species such as butterflies (Stevens, Turlure & Baguette 2010b; Sekar 2012). In vertebrates however, relationships between dispersal distance and a wider variety of life-history traits have been explored primarily for birds, where generally body mass, wingspan or wing morphology and migratory behaviour are shown to be the dominant driving forces (Paradis et al. 1998; Dawideit et al. 2009; Garrard et al. 2012).
We compiled a database of dispersal distance in mammals, spanning a broad taxonomic and geographic range to explore these questions. We used mammals as data availability across the group is relatively high, including the mammal supertree, a recent species-level phylogeny of 5020 extant mammals (Bininda-Emonds et al. 2007; Fritz, Bininda-Emonds & Purvis 2009), and a database of mammalian life-history traits (Jones et al. 2009). Mammals are also ecologically diverse and show a range of sizes and variability across axes of life history that allow robust comparisons to be made across the group. Here, we use this dispersal database to test scaling of dispersal distance across median and maximum measures and use a multipredictor phylogenetic framework to test a number of traits that are thought to predict mammalian dispersal distance. We also examine the predictive power of models and make recommendations for those wishing to predict mammalian dispersal distances when empirical evidence is lacking.
- Top of page
- Materials and methods
- Supporting Information
We found that no single model performed best in describing either maximum or median dispersal distances. Support for a wide range of models across all predictor variables shows that a number of different aspects of a species life history and ecology can be used to predict dispersal distances. This support for many model combinations may reflect the complex nature of the evolution and expression of dispersal as one part of a co-evolving suite of interlinked behavioural, physiological and ecological traits (Ferriere et al. 2000). For example, if selection is to favour dispersal, then individuals must possess the physical ability to disperse and to survive the dispersal process, but they must also be able to successfully reproduce upon arrival in a suitable environment (Johnson & Gaines 1990).
Supporting evidence for multiple correlates of dispersal has recently been demonstrated by Lyons, Wagner & Dzikiewicz (2010). Using data from the fossil record, they found significant relationships between several life-history traits, such as gestation length and maximum life span and the distance moved by the range centroid of North American mammals during the glacial–interglacial cycles of the Late Pleistocene epoch. In addition, Stevens et al. (2012) recently found correlations between dispersal ability in butterflies and a suite of demographic traits. Therefore, the correlations between multiple life-history parameters and dispersal distance found in this study are not unexpected, but the mechanisms behind the co-evolution of ‘dispersive traits’ are unclear and beyond the remit of this study. The traits used in our models vary in the way that they are hypothesized to interact with dispersal: traits such as large body size may aid the dispersal process by lowering the energetic cost of dispersal and decreasing predation risk, some traits such as population density are thought to be drivers of dispersal, and some traits such as home range size and age at sexual maturity may be a reflection of dispersal and colonization ability. Thus, our study correlates traits to allow for prediction rather than identifying mechanisms for dispersal.
Although no single model outperformed all others, body mass and home range size consistently emerged as important predictors of dispersal ability in accordance with findings of previous studies (Van Vuren 1998; Sutherland et al. 2000; Bowman, Jaeger & Fahrig 2002; Dawideit et al. 2009; Sekar 2012). We do not find a strong effect of trophic level (cf. Sutherland et al. 2000). This is likely due to the inclusion of phylogeny in the models. As trophic level shows strong phylogenetic signal, studies that fail to account for the non-independence of species may confound results by introducing pseudoreplication into the analyses, for example species with smaller home range size tend to be herbivores and also show lower dispersal distance (Purvis et al. 2005). In single predictor models, home range size was a better predictor of dispersal distance than body mass (home range size: Dmed r2 = 0·798, Dmax r2 = 0·672; body mass: Dmed r2 = 0·307, Dmax r2 = 0·373). However, the amount of variance in maximum dispersal distance explained by home range size was still lower than found in a previous study by Bowman, Jaeger & Fahrig (2002). The high weighting of these terms may reflect the ability of species to cross landscapes, the spatial scale at which landscape is perceived and/or energetic constraints to dispersal distance imposed by size, as these are factors that are mediated by body mass and reflected in home range size and as such make these variables useful in predicting dispersal distance (Holt 2003; Jetz et al. 2004; Lester et al. 2007). In models of median dispersal distance, weaning age and its quadratic term were also found to show a significant and consistent relationship across the model set. The prominence of the quadratic term hints at a possible trade-off between life-history specialization and dispersal ability, with those at the extreme ends of life-history variation showing reduced expression of dispersal ability.
Interestingly, geographic range size was found to be the term with highest model weighting across models of maximum dispersal distance but among the lowest weighted in models of median dispersal distance. This may be related to the capacity of species for LDD, a trait that can facilitate access to new habitats which subsequently leads to range expansion and hence observed range size (Kot, Lewis & van den Driessche 1996; Gaston 2003; Petrovskii & Morozov 2009). The contrast in predictive power of geographic range between models of maximum and median dispersal distances indicates that different parts of the dispersal distance distribution may have evolved independently and under different selection pressures (Stevens et al. 2012). For example, short-distance dispersal undertaken by large proportions of the population may be selected for inbreeding avoidance, whereas longer movement distances undertaken by relatively few individuals may be selected for based on the benefit of finding empty territories (Lambin, Aars & Piertney 2001; Higgins, Nathan & Cain 2003; Ronce 2007). The hypothesis that median and maximum dispersal distances may be under differing selection processes is given weight by the high variable weightings of population density and weaning age in models of maximum dispersal distance, a finding not echoed for models of median dispersal distance. Although the confidence intervals of the slopes for these relationships are broad, they hint at some interesting underlying drivers of dispersal. In the case of weaning age, this may be linked to the importance of competitive ability upon arrival in new habitats (Burton, Phillips & Travis 2010).
Both maximum and median dispersal distances show evidence of strong phylogenetic conservatism. In addition, the relationship between these measures shows phylogenetic structure: without accounting for phylogeny, the maximum and median dispersal distances are proportional, but the inclusion of phylogeny shows that maximum dispersal distance increases disproportionately with median dispersal distance. Phylogenetic information may therefore be used to guide predictions of dispersal distances for species where data are lacking but where information for closely related species is available (Fisher & Owens 2004; Thomas 2008) and the inclusion of phylogeny in any future comparative models of dispersal ability is recommended.
The strength of this phylogenetic signal is perhaps more than would be expected based on studies examining the evolution of dispersal kernels across populations of the same species (Murrell, Travis & Dytham 2002; Burton, Phillips & Travis 2010; Travis, Smith & Ranwala 2010) and therefore likely masks within-species flexibility in response to the often diverse landscapes and locations within the range which impose differing dispersal costs. Comparative analyses of the shape of dispersal kernel might therefore reveal greater phylogenetic lability (Simmons & Thomas 2004). For example, evidence of changes in both dispersal kernel shape and frequency of ‘disperser’ morphs have been found in cane toads (Phillips et al. 2008), wing-dimorphic bush crickets (Simmons & Thomas 2004) and European butterflies (Stevens, Pavoine & Baguette 2010a).
Cross-validation of the predictive accuracy of models showed that highly weighted models could also predict dispersal distances to varying extents for the species excluded from the data set for testing purposes. Therefore with knowledge of relatively few life-history and ecological traits, predictions of dispersal distance can be made where empirical data are lacking, given the set of preferred models presented in this study. The ability of models to accurately predict observed dispersal distances varies across species (Fig. 4, Table 5 in Appendix S3). It is not immediately obvious as to the cause of this variation. Overpredictions may (among a number of reasons) simply be due to a lack of data (i.e. we have not yet captured the ‘true’ maximum or median dispersal distance of the species) or the location of the species in patchy or highly modified landscapes. Underpredictions also provide an interesting avenue for exploration as species are dispersing farther than would be expected given their traits. These include wide-ranging species with a tendency to become classified as invasive outside the native range (e.g. Sciurus carolinensis, the eastern grey squirrel in the UK, and Vulpes vulpes, the red fox in Australia).
The identification of a relationship between median and maximum dispersal distances also provides the opportunity to predict longer distance movements from the more readily observed and available shorter dispersal distances. There is, however, a degree of scatter around the relationship such that relationships between median and maximum dispersal distances may vary over several orders of magnitude from the line of best fit. While comparative predictions are useful at broad taxonomic levels across many groups, however, further work must be undertaken to assess the quality and quantity of data necessary to make accurate predictions for specific species in a landscape context. The relationship of average dispersal distance with ‘true’ LDD is unknown as capturing the tail of the dispersal kernel is extremely difficult, especially from observational studies (Koenig, VanVuren & Hooge 1996). Tests that combine indirect genetic data and data from direct or observational studies may help improve estimates of LDD and have been employed at the local scale (Vandewoestijne & Baguette 2004) but are currently unavailable at the macroscale required for this study (Ferriere et al. 2000; Stevens et al. 2010b).
One criticism levelled at direct measures of dispersal distance is that they frequently fail to capture rare LDD events (Koenig, VanVuren & Hooge 1996; Kot, Lewis & van den Driessche 1996) and that using gene flow measures provides a more robust way of capturing the tail of the dispersal kernel. Indirect measures of gene flow, such as FST, that quantify variance in allele frequency between populations can be combined with measure of distance between sampled populations in pairwise comparisons to create models of isolation by distance (IBD; Wright 1965; Slatkin 1987; Rousset 1997) from which dispersal rates can be inferred. However, such models are only effective at the spatial scale at which the samples are taken. Ascertaining estimates for LDD can be confounded when two populations separated by great distances both receive migrants from the same intervening populations (Lester et al. 2007). Although these distantly separated populations may exchange no individuals, they show low genetic variance owing to the homogenizing effect of migration from the same source, which can lead to an incorrect inference of high migration distances (Whitlock & McCauley 1999). Incomplete knowledge of the spatial structure of habitats separating populations may also confound IBD estimates as many of the common statistical tests are based on a non-spatial null model (Raybould et al. 2002; Meirmans 2012). These caveats are not insurmountable, and gene flow measures have much to offer the study of dispersal (Waser & Hadfield 2011); however, these problems mean that amount of data available to create robust IBD models is limited, and therefore, the number of species which can be studied is relatively small.
Combining predictions of dispersal distance with generation time can give realistic estimates about how far and how quickly species can track changing climates and move between protected areas. Some small-bodied species, however, may offset low dispersal distances with fast generation times which may enable them to keep pace with climate change or adapt to changing conditions. Cross-validation of models identified species that were consistently over- or underpredicted in their dispersal ability (Fig. 4, Table 5 in Appendix S3). This may be a true reflection of a mismatch between vagility and life history or may be due to the failure of empirical studies to capture the true maximum dispersal distance of the species or as a result of landscape effects modifying dispersal distances seen from empirical studies. Species that fail to disperse as far as would be expected given their life history warrant further field research to identify those that are truly limited in dispersal capability. Although there is no clear taxonomic pattern to which species are overpredicted, it can be inferred that these species may be at higher risk from the impacts of climate change, as they show lower vagility than organisms of similar size and life-history speed. Finally, species range maps can be combined with information of speed of climate change in that area to identify species that are poor dispersers given their size, range and life history and also inhabit areas likely to experience high velocity of climate change (Loarie et al. 2009). Several plant-focussed SDMs have started to incorporate migration limits and dispersal estimates into models (Engler & Guisan 2009; Thuiller et al. 2009). Our study allows these techniques to be extended to mammals where a better understanding of how far species can disperse could help us to understand drivers of extinction risk and improve conservation planning, especially when predicting the impacts of climate change.