Etat de publication
Ce manuscrit a ´et´e soumis `a Evolution le 05/11/2014, rejet´e avec re-soumission autoris´ee le 15/12/2014. Nous l’avons re-soumis `a Evolution le 31/03/2016, en le proposant comme manuscrit “compagnon” de Bonnet-Lebrun et al. (2016). Cette ´etude utilise un mod`ele simi-laire mais proc´edant en temps direct pour ´etudier les effets de la structuration spatiale des archipels sur les patrons macro´evolutifs (tempo de branchement des phylog´enies) et patrons de composition des communaut´es (distributions d’abondance d’esp`eces).
The effects of archipelago spatial structure on island diversity and endemism : predictions from a spatially-structured neutral model
Fanny Gascuel1,2,3,4,∗, Fabien Laroche1,5, Anne-Sophie Bonnet-Lebrun1,6 and Ana S.L. Rodrigues1
1 Centre d’ ´Ecologie Fonctionnelle et ´Evolutive, UMR 5175, Montpellier, France ;
2 Center for Interdisciplinary Research in Biology, CNRS UMR 7241, Coll`ege de France, Paris, France ;
3 Sorbonne Universit´es, UPMC Univ Paris 06, ED 227, Paris, France ;
4 Institut de Biologie de l’ ´Ecole Normale Sup´erieure, CNRS UMR 8197, Ecole Normale Sup´erieure, Paris, France ;
5 AgroParistech ENGREF, Paris, France ;
6 Department of Zoology, University of Cambridge, Cambridge, UK ;
∗ Author for correspondence
Running title : Archipelago spatial structure and diversification
Data Archival Location : The algorithms of our models and input data files will be deposited on a Dryad repository at http ://datadryad.org.
Abstract
Islands are particularly suited to testing hypotheses about the ecological and evolutionary mechanisms underpinning community assembly. Yet the complex spatial arrangements of real island systems have received little attention from both empirical studies and theoretical models. Here, we investigate the extent to which the spatial structure of archipelagos affects species diversity and endemism. We start by proposing a new spatially-structured neutral model that explicitly considers archipelago structure, and then investigate its predictions under a diversity of scenarios. Our results suggest that considering the spatial structure of archipelagos is crucial to understanding their diversity and endemism, with structured island systems acting both as “museums” and “cradles” of biodiversity. The dynamics of diversification within spatially-structured archipelagos may change the traditionally expected pattern of decrease in species richness with distance from the mainland, even potentially leading to increasing patterns for taxa with high speciation rates in archipelagos
off species-poor continental areas. Our results also predict that, within spatially-structured archipelagos, metapopulation dynamics and evolutionary processes can generate higher diversity on islands more centrally placed than at the periphery. We derive from our results a set of theoretical predictions, all potentially testable with empirical data.
Keywords : biodiversity, biogeography, spatially explicit model, neutral model, protracted speciation, species richness.
Introduction
Islands are particularly suited to the development of hypotheses about the ecological and evo-lutionary mechanisms underpinning community assembly, and for testing the corresponding predictions. The Theory of Island Biogeography (abbreviated “TIB” hereafter ; MacArthur et Wilson, 1967) emphasised the role of dispersal and extinction in community assembly, re-sulting in two well-known predictions. First, islands further from the mainland tend to have lower species richness, because isolation reduces immigration rates and the lower likelihood of rescue effects increases chances of extinction (MacArthur et Wilson, 1967; Losos, 2010). Second, larger islands tend to have higher species richness, because their larger populations have lower extinction rates and their wider interception areas increase colonisation chances (MacArthur et Wilson, 1967; Losos, 2010).
These predictions were consistent with some empirical examples (e.g., Diamond et Mayr, 1976; Kreft et al., 2008; Lep`ere et al., 2013), but not with others (e.g., Rey, 1981; Heaney, 1984; Milne et Forman, 1986; Kadmon et Pulliam, 1993; Hecnar et al., 2002; Ren et al., 2009). Several (non-mutually exclusive) explanations have been proposed for the lack of consistency, including : biodiversity on islands may not be at equilibrium (due to young island age, strong isolation reducing immigration rates, or stochastic abiotic disturbances ; e.g., MacArthur, 1972; Wilcox, 1978; Whittaker et al., 2008) ; island characteristics other than area affect their carrying capacity (e.g., elevation, habitat composition and heterogeneity ; Dueser et Brown, 1980; Milne et Forman, 1986; Peck et al., 1999; Kallimanis et al., 2009; Carstensen et al., 2011) ; colonisation rates may vary across islands (e.g., because of ocean currents or
winds ; Gill, 1976; Rey, 1981) and among species (because of differences in dispersal abilities ; e.g., Wenner et Johnson, 1980; King, 1988) ; and intra-specific interactions affect extinction rates and islands’ carrying capacity (e.g., Moulton et Pimm, 1983; Lomolino, 1984; Hecnar et al., 2002; Gravel et al., 2011).
However, even after taking into account island characteristics, species ecology and intra-specific interactions, TIB may still fail to predict empirical island diversity by overlooking the long-term contribution of evolutionary processes to community assembly in archipelagos. Indeed, speciation has long been known to contribute to shape current patterns of species diversity (e.g., Lack, 1976). Accordingly, it is taken into account in the recently proposed Unified Neutral Theory of Biodiversity and Biogeography (Hubbell, 2001) : building from the hypotheses of migration-drift introduced by the TIB, speciation was explicitly added as a main process shaping communities at long time scales. In the original version of neutral theory (Hubbell, 2001), speciation was restricted to the broader metacommunity (analogous to the mainland in the TIB), with the diversity of local communities (analogous to islands in the TIB) determined solely by colonisation and demographic stochasticity. However, empirical studies have shown that rapid speciation within islands may explain an important part of present species diversity in archipelagos (Schluter, 2000b; Price et al., 2010).
In the first analysis accounting for local speciation in neutral theory, Rosindell et Phillimore (2011) recently built a neutral model consisting of a mainland and an island connected by limited dispersal, where restricted gene flow results in local speciation (i.e. island endemics). Like in classical Island Biogeography, Rosindell and Phillimore’s model considers a single island (or an archipelago modelled as if its internal spatial structure had no effect). However, islands are seldom alone, being generally part of archipelagos with various spatial configura-tions. Neighbouring islands likely affect the dispersal and extinction rates of any individual island, through stepping-stone effects and metapopulation dynamics (e.g., Rey, 1981; Milne et Forman, 1986; King, 1988; Hanski, 1998). Beyond its impact on migration and drift, the spatial structure of archipelagos is also expected to influence gene flow among islands, and therefore local speciation (Coyne et Orr, 2004). Overall, then, archipelago structure should affect the equilibrium between migration, speciation and extinction, and therefore the diver-sity and endemism of individual islands as well as of the broader archipelagos (as found in the firsts empirical analyses of diversity patterns at the archipelago-scale, e.g. Cabral et al.,
2014; Warren et al., 2015).
Recent advances in neutral models have emphasised the role of spatial structure on commu-nity assemblage, by considering communities as networks of spatial patches interlinked by limited dispersal (e.g., Economo et Keitt, 2008; Muneepeerakul et al., 2008; Warren et al., 2010; Desjardins-Proulx et Gravel, 2012b,a; Bonnet-Lebrun et al., 2016). These analyses ad-dress the relationship between spatial isolation and speciation. However, by not accounting for an external source of propagules (i.e. a mainland) those works have not generated explicit feedback towards the TIB, despite being highly relevant to true island systems.
Here, we build from these recent advances in community ecology (e.g., Hubbell, 2001; Eco-nomo et Keitt, 2008; Rosindell et al., 2008, 2010; Rosindell et Phillimore, 2011; Bonnet-Lebrun et al., 2016) to investigate how the interaction between the internal spatial structure of archipelagos and the speciation process within islands through limited gene flow can im-pact diversity and endemism patterns in island systems. First, we propose what we term a spatially-structured” model : a new neutral model that explicitly considers how island com-munities might assemble and evolve when archipelagos are structured into multiple islands. We consider the particular case of an archipelago encompassing aligned islands, comparing the diversity and endemism of communities obtained through the spatially-structured model with those obtained through a similar but unstructured model (a “null hypothesis” model, without internal spatial structure). By comparing the results of these models, we derive predictions for : the effects of spatial structure on the overall diversity and endemism of archipelagos ; the effects of belonging to an archipelago on the diversity and endemism of in-dividual islands ; and the extent to which these predictions are affected by mainland diversity and by speciation rates in the islands. To investigate the contribution of speciation to these effects, we also look at the predictions of the two above models without island speciation (i.e. considering that speciation only occurs in the mainland, as in Hubbell, 2001).
Methods Models
The spatially-structured model
mainland and exchanging migrants among themselves. The mainland is modelled as a large pool of individuals with a pre-defined species-abundance distribution consistent with neutral theory (Hubbell, 2001) and assumed to be constant throughout the coalescence process (see Appendix 1). Islands are defined as points in space, each containing a set of individuals that can move without restriction within the island. As in previous neutral community models (Hubbell, 2001; Rosindell et Phillimore, 2011), the community dynamics is modelled through death-replacement cycles, so that the total number of individuals in each island (and thus in the archipelago) is constant through time. All individuals have the same probabilities of birth, death, dispersal-limited migration and speciation irrespective of their species (the neutral assumptions ; Hubbell, 2001).
To model the neutral dynamics, our algorithm (further detailed in Appendix 2) proceeds in two steps, as proposed by Rosindell et al. (2008) and Rosindell et Phillimore (2011). The first step builds (backward in time) the genealogy of a sample of the individuals living within the archipelago at present time, by reconstructing the coalescence of these individuals and following the migration of their ancestral lineages among islands back in time, until reaching the immigration events from the mainland. The second step browses (forward in time) the history of this genealogy, determining where and when speciation events take place. The overall procedure assigns a species to each present individual, determining the equilibrium species abundance distribution across islands of the archipelago.
If they were modelled forward in time, the genealogical relationships between individuals in our simulations would be generated as follows : the death of an individual in an island opens a gap that is instantaneously colonised by a newly born propagule. This propagule may come from the same island, or be a migrant, either from another island, or from the mainland. Its origin is chosen following a lottery game, with probabilities determined by a matrix of connectivity between all landmasses (all islands in the archipelago and the mainland), M . The matrix M establishes, for each pair of landmasses (i, j), the probability M [i, j] for a vacant site in landmass i to be colonized by a propagule coming from landmass j. To obtain these probabilities, we combined the matrix of geographic distances between all landmasses (considered as points on a map), their carrying capacity, and a fat-tailed dispersal kernel (Clark et al., 1999; Chave et Leigh, 2002) : overall, more propagules originate from closer and larger landmasses, and the larger a landmass the larger the propagule fraction
originating from within it. Further details are provided in Appendix 3. In our model, the first step of a community simulation is done by simulating (backwards) the coalescent process corresponding to this forward-in-time algorithm. For a sample of the individuals existing at present time, this coalescence process generates a genealogy for the corresponding lineages, with information on the geographic location of these tracked lineages at each time point in the past.
In the second step, one browses the generated genealogical tree forward in time and places speciation events on branches. Speciation within the archipelago is assumed to be “protrac-ted”, meaning it takes time and is delayed by gene flow (Rosindell et al., 2010; Rosindell et Phillimore, 2011). It is modelled as population-scale phenomenon : when the individuals of a given species within a given island (i.e., a population) have differentiated long enough to undergo speciation, they all switch from their ancestral to a new species (never seen before either in the mainland or the archipelago). The speciation rate of each population, ν[i], de-creases with the rate of immigration into the island i where the population is located, due to gene flow slowing down genetic divergence. We define T spc[i] = 1/ν[i] the expected time (measured in number of birth-death cycles) needed to complete speciation on island i for the population of any species. T spc[i] is implemented by stating T spc[i] = T spc + delay[i], where T spc is the baseline time needed to complete speciation and delay[i] is a random delay due to gene flow which increases linearly with the proportion of external propagules (coming from other islands or from the mainland) received by island i (see Appendix 2 for more details). Hence, in this model each population has an internal “speciation clock” indicating the time remaining before speciation. When the clock of a population in island i reaches zero, all respective tracked lineages become a new species, and their clock is reset to T spc[i]. When a tracked lineage migrates (either from another island or from the mainland) into an island i where no conspecific tracked lineage is already present, it establishes a new population, whose speciation clock is also set to T spc[i].
As in Rosindell et Phillimore (2011), we used coalescence as a modelling approach, given its high computing efficiency Rosindell et al. (2008, 2010). However, this requires two im-portant approximations potentially affecting the times needed to complete speciation. First, we considered that only island connectivity (inversely related to overall propagule pressure) affects the time needed to complete speciation in each island, but in reality it should also
be influenced by species’ abundances : in any given island, a lineage belonging to a species abundant at the scale of the archipelago should take longer to undergo speciation than one belonging to a rare species, due to stronger gene flow. Because only a small fraction of all individuals are tracked by the coalescent process (especially near the base of the genealogical tree), the abundances of species are not known when browsing the genealogical tree forward in time to determine speciation events. The times needed to complete speciation are therefore necessarily approximated in our algorithm. Second, given that only a fraction of the lineages are being tracked by the coalescence process, when a lineage newly arrives to an island i, we do not know if untracked individuals belonging to the same species are already present. If this was the case, the speciation clock of the newly arriving lineage should be set to the value of the speciation clock of the unobserved resident population, which would stand below T spc[i]. We however expect these two approximations to have little and/or a reinforcing effects on the patterns that we observe (see discussion in Appendix 2) ; furthermore, we tested this expectation by performing a sensitivity analysis, comparing our results to those obtained for the same system simulated through a forward-in-time algorithm, which allows to keep track of all individuals at all times (modified from Bonnet-Lebrun et al., 2016). This analysis showed that the approximations made in the coalescent model do not impact our results and conclusions (see Appendix 4).
Rosindell et Phillimore (2011) modelled cladogenesis within islands through point speciation, but we opted not to do so in order to : (i) model speciation through only one, explicit and realistic, process ; and (ii) focus on how this process of speciation through limited gene flow interacts with archipelago spatial structure to shape the patterns of diversity and endemism in island systems. Instead, we modelled islands as points containing panmictic communities, thus not subject to speciation through limited gene flow. Incorporating cladogenesis within islands could however be have been done (at a computational cost) using the same spatially-structured modelled : it would have sufficed to account for the internal spatial structure of individual islands by modelling them as clusters of points.
In our analyses, we focused on a simple one-dimensional geographic configuration, where the archipelago consists of eight islands at progressively larger distances to the mainland (Fig. 1A). The islands are equal-sized, with 6,000 individuals each, of which 10% were sampled, resulting in 4,800 present individuals tracked by the coalescence process. The mainland was
modelled as being much larger than the islands, with a carrying capacity of 1,000,000 indivi-duals. Its diversity, and thus that of propagules migrating from the mainland to the islands, was determined by the fundamental biodiversity number, θ, set at 60. The baseline expected time to protracted speciation within archipelagos was set at T spc = 25 million birth-death cycles. In an isolated metacommunity of 48,000 individuals, it corresponds to about 520 generations needed to complete one speciation cycle.
To investigate how distance from the mainland affects the predicted characteristics of island communities, we considered several scenarios of distance between the mainland and (the centroid of) the archipelago. Distance was measured relatively to the dispersal ability of the individuals, modelled as the width c of the fat-tailed dispersal kernel (given in Appendix 3) : the higher c, the further individuals tend to disperse. We re-scaled geographic distances, dividing them by the value of parameter c (maintained constant at c = 55) ; distances are thus expressed in units of width of the dispersal kernel, c. We considered eight scenarios of archipelago centroid distance from the mainland (Fig. 1) : 5, 14, 23, 32, 41, 50, 59 or 68 distance units. Adjacent islands within an archipelago were separated by one distance unit (e.g., in the archipelago whose centroid is at 68 distance units, islands were at 64.5, 65.5, 66.5., 67.5, 68.5, 69.5, 70.5, and 71.5 distance units from the mainland).
For each set of parameters, we created 100 replicates, and then obtained : average total species richness ; average number of species endemic to the archipelago (E) ; and average number of single-island endemics (SIE).
In addition, we tested whether the results are affected by the carrying capacity of archipelagos, by comparing our main results with those obtained with archipelagos of smaller carrying capacities (9,600 individuals ; Appendix 5).
Analyses on the simulated data were done in R (R Development Core Team, 2012). Our spatially-structured model was coded in Java language, and will be available on a Dryad repository at http ://datadryad.org.
The null model : the unstructured archipelago
To investigate the effects of spatial structure on the predictions of diversity and endemism on archipelagos, we created a null model equal in all to the structured model described above but without an internal archipelago spatial structure. This unstructured archipelago was
modelled as a point in space, containing the same number of individuals as the structured archipelago (48,000 individuals, 10% of which sampled and tracked through coalescence), but where individuals can move without restriction (similarly to single islands in the structured archipelago). Hence, the matrix of connectivity in this case includes only the fraction of propagules coming from within the archipelago vs. from the mainland. To ensure differences between the results for the structure and unstructured models derive solely from the effect of spatial structure, we set the fraction of mainland propagules as equal to that arriving to the corresponding structured archipelago (different for each scenario of distance to mainland). Protracted speciation occurs in the unstructured archipelago under the same process as in islands of a structured archipelago, with T spc = 25 million birth-death cycles for 48,000 individuals. Here, however, individuals of an endemic species are panmictic, all synchronized in their speciation process, preventing diversification from an endemic lineage. Therefore, unlike in the model by Rosindell et Phillimore (2011), there is no cladogenesis within the unstructured archipelago.
Model variants without speciation within archipelagos
For both structured and unstructured models, we implemented a variant without speciation within the archipelagos. This allowed us to understand the extent to which the effects of spatial structure on island and archipelago diversity are determined by speciation vs. by mi-gration and drift. It was implemented by modifying the second step of our algorithm : in this case, we use the history of the genealogy of the sampled individuals to define their species identities by simple succession from their mainland ancestor. Conceptually, this also corres-ponds to a scenario with an infinite time to protracted speciation, T spc. The unstructured model without speciation is akin to the original neutral model by Hubbell (2001).