Model Of Oceanic Island Biogeography Research Articles

Early diversification dynamics in a highly successful insular plant taxon are consistent with the general dynamic model of oceanic island biogeography

AbstractThe general dynamic model (GDM) of oceanic island biogeography views oceanic islands predominantly as sinks rather than sources of dispersing lineages. To test this, we conducted a biogeographic analysis of a highly successful insular plant taxon, Cyrtandra, and inferred the directionality of dispersal and founder events throughout the four biogeographical units of the Indo‐Australian Archipelago (IAA), namely Sunda, Wallacea, Philippines, and Sahul. Sunda was recovered as the major source area, followed by Wallacea, a system of oceanic islands. The relatively high number of events originating from Wallacea is attributed to its central location in the IAA and its complex geological history selecting for increased dispersibility. We also tested if diversification dynamics in Cyrtandra follow predictions of adaptive radiation, which is the dominant process as per the GDM. Diversification dynamics of dispersing lineages of Cyrtandra in the Southeast Asian grade showed early bursts followed by a plateau, which is consistent with adaptive radiation. We did not detect signals of diversity‐dependent diversification, and this is attributed to Southeast Asian cyrtandras occupying various niche spaces, evident by their wide morphological range in habit and floral characters. The Pacific clade, which arrived at the immaturity phase of the Pacific Islands, showed diversification dynamics predicted by the island immaturity speciation pulse model (IISP), wherein rates increase exponentially, and their morphological range is controlled by the least action effect favoring woodiness and fleshy fruits. Our study provides a first step toward a framework for investigating diversification dynamics as predicted by the GDM in highly successful insular taxa.

Oceanographic gradients explain changes in the biological traits of nesting seabird assemblages across the south-eastern Pacific

Seabirds are top predators in coastal and pelagic ecosystems that forage at sea but return to land regularly during the breeding season (i.e., central place foragers). This unique life history strategy is directly related to their biological traits and helps define their role as top predators in marine systems. We analysed the effects of physiographic characteristics of nesting islands (area, elevation, and distance from the continent, as predicted by the General Dynamic Model of Oceanic Island Biogeography) and oceanographic variables from waters surrounding nesting islands that tend to be characterised by steep gradients (temperature, salinity, and primary productivity) on the trait diversity of nesting seabird assemblages on islands of the south-eastern Pacific Ocean. Four biological traits related to species’ life history and feeding strategy were used to calculate two indices, the Functional Richness and Rao’s Quadratic entropy. We used fourth-corner and RLQ analysis to determine the relationship between biological traits and environmental variables. Island physiography and primary productivity in the waters surrounding nesting islands significantly affected seabird trait diversity, which gradually decreased from Chilean coastal islands to the distant Polynesian Islands. The traits for body mass and clutch size showed a significant positive relationship with primary productivity. We identified three assemblages of seabirds that had contrasting trait structures. These were defined as the Galapagos, Coastal Chile, and south-eastern Oceanic islands assemblages, and reflected the adaptations of three different species pools to specific oceanographic conditions. Our results suggest that food-related constraints might be one of the most critical environmental filters that shaped the current trait structure of nesting seabird assemblages on the islands in the eastern South Pacific Ocean.

Macaronesia as a Fruitful Arena for Ecology, Evolution, and Conservation Biology

Research in Macaronesia has led to substantial advances in ecology, evolution and conservation biology. We review the scientific developments achieved in this region, and outline promising research avenues enhancing conservation. Some of these discoveries indicate that the Macaronesian flora and fauna are composed of rather young lineages, not Tertiary relicts, predominantly of European origin. Macaronesia also seems to be an important source region for back-colonisation of continental fringe regions on both sides of the Atlantic. This group of archipelagos (Azores, Madeira, Selvagens, Canary Islands, and Cabo Verde) has been crucial to learn about the particularities of macroecological patterns and interaction networks on islands, providing evidence for the development of the General Dynamic Model of oceanic island biogeography and subsequent updates. However, in addition to exceptionally high richness of endemic species, Macaronesia is also home to a growing number of threatened species, along with invasive alien plants and animals. Several innovative conservation and management actions are in place to protect its biodiversity from these and other drivers of global change. The Macaronesian Islands are a well-suited field of study for island ecology and evolution research, mostly due to its special geological layout with 40 islands grouped within five archipelagos differing in geological age, climate and isolation. A large amount of data is now available for several groups of organisms on and around many of these islands. However, continued efforts should be made toward compiling new information on their biodiversity, to pursue various fruitful research avenues and develop appropriate conservation management tools.

Automated Discovery of Relationships, Models, and Principles in Ecology

Ecological systems are the quintessential complex systems, involving numerous high-order interactions and non-linear relationships. The most used statistical modeling techniques can hardly accommodate the complexity of ecological patterns and processes. Finding hidden relationships in complex data is now possible using massive computational power, particularly by means of artificial intelligence and machine learning methods. Here we explored the potential of symbolic regression (SR), commonly used in other areas, in the field of ecology. Symbolic regression searches for both the formal structure of equations and the fitting parameters simultaneously, hence providing the required flexibility to characterize complex ecological systems. Although the method here presented is automated, it is part of a collaborative human–machine effort and we demonstrate ways to do it. First, we test the robustness of SR to extreme levels of noise when searching for the species-area relationship. Second, we demonstrate how SR can model species richness and spatial distributions. Third, we illustrate how SR can be used to find general models in ecology, namely new formulas for species richness estimators and the general dynamic model of oceanic island biogeography. We propose that evolving free-form equations purely from data, often without prior human inference or hypotheses, may represent a very powerful tool for ecologists and biogeographers to become aware of hidden relationships and suggest general theoretical models and principles.

Sars: an R package for fitting, evaluating and comparing species–area relationship models

The species–area relationship (SAR) constitutes one of the most general ecological patterns globally. A number of different SAR models have been proposed. Recent work has shown that no single model universally provides the best fit to empirical SAR datasets: multiple models may be of practical and theoretical interest. However, there are no software packages available that a) allow users to fit the full range of published SAR models, or b) provide functions to undertake a range of additional SAR‐related analyses. To address these needs, we have developed the R package ‘sars’ that provides a wide variety of SAR‐related functionality. The package provides functions to: a) fit 20 SAR models using non‐linear and linear regression, b) calculate multi‐model averaged curves using various information criteria, and c) generate confidence intervals using bootstrapping. Plotting functions allow users to depict and scrutinize the fits of individual models and multi‐model averaged curves. The package also provides additional SAR functionality, including functions to fit, plot and evaluate the random placement model using a species–sites abundance matrix, and to fit the general dynamic model of oceanic island biogeography. The ‘sars’ R package will aid future SAR research by providing a comprehensive set of simple to use tools that enable in‐depth exploration of SARs and SAR‐related patterns. The package has been designed to allow other researchers to add new functions and models in the future and thus the package represents a resource for future SAR work that can be built on and expanded by workers in the field.

Eurya stigmosa (Theaceae), a new and extinct record for the Calabrian stage of Madeira Island (Portugal): 40Ar/39Ar dating, palaeoecological and oceanic island palaeobiogeographical implications

The general dynamic model of oceanic island biogeography (GDM) predicts the immigration, speciation and extinction of terrestrial biota through geological time on oceanic islands. Additionally, the glacial-sensitive model of island biogeography (GSM) also predicts extinction due to eustatic and climate change within islands. However, well-documented and natural pre-Holocene plant extinctions are almost unknown for oceanic islands worldwide. To test these predictions, we have sampled the Early Pleistocene Porto da Cruz lacustrine and fluvial sediments for plant fossils that could confirm the GDM and GSM extinction predictions. Additionally, two new 40Ar/39Ar geochronological analyses were performed, constraining the age of the sediments to 1.3 Ma (Calabrian). Among the fossils, Eurya stigmosa (R.Ludw.) Mai (Theaceae) seeds were recognised and studied by scanning electron microscopy (SEM). E. stigmosa is the first report of a natural (non-anthropogenic) extinct plant in the fossil record for Madeira Island, and for an oceanic island, confirming the GDM and GSM predictions. Eurya spp. palaeobiogeography indicates wider distribution in Europe until the end of the Pliocene (2.58 Ma), becoming extirpated to small refugia and extinct thereafter. The Madeiran record expands the formerly unknown presence of E. stigmosa to the Macaronesian realm. The new dating of the deposit at 1.3 Ma (Calabrian) means that E. stigmosa in Madeira was already in a refugium. The extinction in Madeira is most probably a combination of island ontogeny and climate change due to Pleistocene glaciations. The palaeoecological role of this extinct shrub or tree is currently unknown, but it was a probably an element of the Madeiran laurel forest, as this community was already present in Madeira at least 1.8 My ago. This new information corroborates the predictive power of GDM and GSM and adds a new view on the importance of studying oceanic island palaeobotany, specially palaeocarpofloras.

High endemism of mosquitoes on São Tomé and Príncipe Islands: evaluating the general dynamic model in a worldwide island comparison

AbstractThe archipelago of São Tomé and Príncipe is a major centre of endemism for vertebrates but, to date, arthropods remain poorly studied, with the exception of mosquitoes due to their medical and economic relevance. Previous studies, over 20 years old, recorded 26 species of mosquitoes for São Tomé and 14 for Príncipe, of which six and two were endemic to each island respectively.We collected mosquito larvae and adults on both islands and found three species as a first record, putting the number of mosquito species collected at least once on São Tomé at 29 and 15 for Príncipe. We compared the mosquito richness on São Tomé and Príncipe islands to 71 other oceanic islands represented within 20 archipelagoes worldwide.We used the general dynamic model of oceanic island biogeography and the associated ATT2(for Area+Time+Time2) model to explain two different metrics: (i) the richness of the single‐island endemics and (ii) the richness of archipelago endemics. We also included other island characteristics in our model, such as isolation and latitude.Our data compilation and comparative analyses revealed an overall low richness of endemic mosquitoes on islands, with the highest number of single‐island endemics found on São Tomé Island.As predicted, we found a positive relationship between our two richness metrics and the general dynamic model ATT2, although the model with the area (A) alone also explained endemic richness. Isolation was also predictor of mosquito diversification.

Oceanic island biogeography through the lens of the general dynamic model: assessment and prospect.

The general dynamic model of oceanic island biogeography (GDM) has added a new dimension to theoretical island biogeography in recognizing that geological processes are key drivers of the evolutionary processes of diversification and extinction within remote islands. It provides a dynamic and essentially non-equilibrium framework generating novel predictions for emergent diversity properties of oceanic islands and archipelagos. Its publication in 2008 coincided with, and spurred on, renewed attention to the dynamics of remote islands. We review progress, both in testing the GDM's predictions and in developing and enhancing ecological-evolutionary understanding of oceanic island systems through the lens of the GDM. In particular, we focus on four main themes: (i) macroecological tests using a space-for-time rationale; (ii) extensions of theory to islands following different patterns of ontogeny; (iii) the implications of GDM dynamics for lineage diversification and trait evolution; and (iv) the potential for downscaling GDM dynamics to local-scale ecological patterns and processes within islands. We also consider the implications of the GDM for understanding patterns of non-native species diversity. We demonstrate the vitality of the field of island biogeography by identifying a range of potentially productive lines for future research.

Transferring and implementing the general dynamic model of oceanic island biogeography at the scale of island fragments: the roles of geological age and topography in plant diversification in the Canaries

AbstractAimThe general dynamic model (GDM) of oceanic island biogeography integrates rates of immigration, speciation and extinction in relation to a humped trajectory of island area, species carrying capacity and topographic complexity through time, based on a simplified island ontogeny. In practice, many islands have more complex ontogenies, featuring surfaces of varying age. Here, we extend the GDM to apply at a local scale within islands, and test the predictions analytically within individual islands.LocationEl Hierro, La Palma and Tenerife (Canary Islands).MethodsFollowing the GDM logic, we derive predictions for the distributions and richness of single island endemics (SIEs) across island landscapes of different age. We test these predictions by means of generalized linear models and binominal tests using gridded species occurrence data for vascular plant SIE species and a set of climatic, topographic and terrain age variables. We also examined phylogenetic divergence times for a subset of endemic lineages.ResultsGeological age, in interaction with slope, and topographic variables, best explained SIE richness at the landscape scale. About 70% of SIEs had ranges strongly biased to, or largely restricted to old terrain. Available phylogenetic divergence times of SIEs of radiated plant lineages suggested an origin on the older parts of the islands. Metrics of anthropogenic disturbance and habitat availability were unrelated to the observed SIE pattern.Main conclusionsOur findings support the hypothesis that SIEs have evolved and accumulated on older and topographically complex terrain, while colonization processes predominate on the youngest parts. These results imply that evolutionary processes shape species distributions at the landscape scale within islands. This opens the perspective of extending the GDM framework to understand processes at a local scale within individual islands.

Geographical, temporal and environmental determinants of bryophyte species richness in the Macaronesian islands.

Species richness on oceanic islands has been related to a series of ecological factors including island size and isolation (i.e. the Equilibrium Model of Island Biogeography, EMIB), habitat diversity, climate (i.e., temperature and precipitation) and more recently island ontogeny (i.e. the General Dynamic Model of oceanic island biogeography, GDM). Here we evaluate the relationship of these factors with the diversity of bryophytes in the Macaronesian region (Azores, Madeira, Canary Islands and Cape Verde). The predictive power of EMIB, habitat diversity, climate and the GDM on total bryophyte richness, as well as moss and liverwort richness (the two dominant bryophyte groups), was evaluated through ordinary least squares regressions. After choosing the best subset of variables using inference statistics, we used partial regression analyses to identify the independent and shared effects of each model. The variables included within each model were similar for mosses and liverworts, with orographic mist layer being one of the most important predictors of richness. Models combining climate with either the GDM or habitat diversity explained most of richness variation (up to 91%). There was a high portion of shared variance between all pairwise combinations of factors in mosses, while in liverworts around half of the variability in species richness was accounted for exclusively by climate. Our results suggest that the effects of climate and habitat are strong and prevalent in this region, while geographical factors have limited influence on Macaronesian bryophyte diversity. Although climate is of great importance for liverwort richness, in mosses its effect is similar to or, at least, indiscernible from the effect of habitat diversity and, strikingly, the effect of island ontogeny. These results indicate that for highly vagile taxa on oceanic islands, the dispersal process may be less important for successful colonization than the availability of suitable ecological conditions during the establishment phase.

Re-evaluating the general dynamic theory of oceanic island biogeography

The general dynamic model of oceanic island biogeography integrates temporal changes in ecological circumstances with diversification processes, and has stimulated current research in island biogeography. In the original publication, a set of testable hypotheses was analysed using regression models: specifically, whether island data for four diversity indices are consistent with the ‘B~ATT2’ model, in which B is a diversity index, A is log(area) and T is time. The four indices were species richness, the number and percentage of single‐island endemic species, and a diversification index. Whether the relationships between these indices and time are unimodal (i.e., ‘hump‐shaped’) was a key focus, based on the characteristic ontogeny of a volcanic oceanic island. However, the significance testing unintentionally used zero, rather than the mean of the diversity index, as the null hypothesis, greatly inflating F‐ values and reducing P‐values compared with the standard regression approach. Here we first re‐analyze the data used to evaluate the general dynamic model in the seminal paper, using the standard null hypothesis, to provide an important qualification of its empirical results. This supports the significance of about half the original tests, the rest becoming non‐significant but mostly suggestive of the hypothesized relationship. Then we expand the original analysis by testing additional, theoretically derived functional relationships between the diversity indices, island area and time, within the framework of the ATT2 model and using a mixed‐effects modelling approach. This shows that species richness peaks earlier in island life‐cycles than endemism. Area has a greater effect on species richness and the number of single‐island endemics than on the proportion of single‐island endemics and the diversification index, and was always better fit as a log–log relationship than as a semi‐log one. Finally, the richness–time relationship is positively skewed, the initial rise happening much more quickly than the later decline.

Accounting for data heterogeneity in patterns of biodiversity: an application of linear mixed effect models to the oceanic island biogeography of spore‐producing plants

The general dynamic model of oceanic island biogeography describes the evolution of species diversity properties, including species richness (SR), through time. We investigate the hypothesis that SR in organisms with high dispersal capacities is better predicted by island area and elevation (as a surrogate of habitat diversity) than by time elapsed since island emergence and geographic isolation. Linear mixed effect models (LMMs) subjected to information theoretic model selection were employed to describe moss and liverwort SR patterns from 67 oceanic islands across 12 archipelagos. Random effects, which are used to modulate model parameters to take differences among archipelagos into account, included only a random intercept in the best‐fit model for liverworts and in one of the two best‐fit models for mosses. In this case, the other coefficients are constant across archipelagos, and we interpret the intercept as a measure of the intrinsic carrying capacity of islands within each archipelago, independently of their size, age, elevation and geographic isolation. The contribution of area and elevation to the models was substantially higher than that of time, with the least contribution made by measures of geographic isolation. This reinforces the idea that oceanic barriers are not a major impediment for migration in bryophytes and, together with the almost complete absence of in situ insular diversification, explains the comparatively limited importance of time in the models. We hence suggest that time per se has little independent role in explaining bryophyte SR and principally features as a variable accounting for the changing area and topographic complexity during the life‐cycle of oceanic islands. Simple area models reflecting habitat availability and diversity might hence prevail over more complex temporal models reflecting in‐situ speciation and dispersal (time, geographic connectivity) in explaining patterns of biodiversity for exceptionally mobile organisms.

Snails on oceanic islands: testing the general dynamic model of oceanic island biogeography using linear mixed effect models

AbstractAimWe collate and analyse data for land snail diversity and endemism, as a means of testing the explanatory power of the general dynamic model of oceanic island biogeography (GDM): a theoretical model linking trends in species immigration, speciation and extinction to a generalized island ontogeny.LocationEight oceanic archipelagos: Azores, Canaries, Hawaii, Galápagos, Madeira, Samoa, Society, Tristan da Cunha.MethodsUsing data obtained from literature sources we examined the power of the GDM through its derivative ATT2 model (i.e. diversity metric = b1 + b2Area + b3Time + b4Time2), in comparison with all the possible simpler models, e.g. including only area or time. The diversity metrics considered were the number of (1) native species, (2) archipelagic endemic species, and (3) single‐island endemic species. Models were evaluated using both log‐transformed and untransformed diversity data by means of linear mixed effect models. For Hawaii and the Canaries, responses of different major taxonomic groups were also analysed separately.ResultsThe ATT2 model was always included within the group of best models and, in many cases, was the single‐best model and was particularly successful in fitting the log‐transformed diversity metrics. In four archipelagos, a hump‐shaped relationship with time (island age) is apparent, while the other four archipelagos show a general increase of species richness with island age. In Hawaii and the Canaries outcomes vary between different taxonomic groups.Main conclusionsThe GDM is an intentionally simplified representation of environmental and diversity dynamics on oceanic islands, which predicts a simple positive relationship between diversity and island area combined with a humped response to time. We find broad support for the applicability of this model, especially when a full range of island developmental stages is present. However, our results also show that the varied mechanisms of island origins and the differing responses of major taxa should be taken into consideration when interpreting diversity metrics in terms of the GDM. This heterogeneity is reflected in the fact that no single model outperforms all the other models for all datasets analysed.

Drivers of diversity in Macaronesian spiders and the role of species extinctions

AbstractAim To identify the biogeographical factors underlying spider species richness in the Macaronesian region and assess the importance of species extinctions in shaping the current diversity.Location The European archipelagos of Macaronesia with an emphasis on the Azores and Canary Islands.Methods Seven variables were tested as predictors of single‐island endemics (SIE), archipelago endemics and indigenous spider species richness in the Azores, Canary Islands and Macaronesia as a whole: island area; geological age; maximum elevation; distance from mainland; distance from the closest island; distance from an older island; and natural forest area remaining per island – a measure of deforestation (the latter only in the Azores). Different mathematical formulations of the general dynamic model of oceanic island biogeography (GDM) were also tested.Results Island area and the proportion of remaining natural forest were the best predictors of species richness in the Azores. In the Canary Islands, area alone did not explain the richness of spiders. However, a hump‐shaped relationship between richness and time was apparent in these islands. The island richness in Macaronesia was correlated with island area, geological age, maximum elevation and distance to mainland.Main conclusions In Macaronesia as a whole, area, island age, the large distance that separates the Azores from the mainland, and the recent disappearance of native habitats with subsequent unrecorded extinctions seem to be the most probable explanations for the current observed richness. In the Canary Islands, the GDM model is strongly supported by many genera that radiated early, reached a peak at intermediate island ages, and have gone extinct on older, eroded islands. In the Azores, the unrecorded extinctions of many species in the oldest, most disturbed islands seem to be one of the main drivers of the current richness patterns. Spiders, the most important terrestrial predators on these islands, may be acting as early indicators for the future disappearance of other insular taxa.

Insight into an island radiation: the Tarentola geckos of the Cape Verde archipelago

AbstractAim To reassess the relationships between Tarentola geckos from the Cape Verde Islands by including specimens from all islands in the range. To determine the variation within forms by sequencing over 400 specimens, thereby allowing the discovery of cryptic forms and resolving some of the issues raised previously. This extensive sampling was also used to shed light on distributions and to explain genetic diversity by comparing the ages and ecological and geological features of the islands (size, elevation and habitat diversity).Location The Cape Verde Islands: an oceanic archipelago belonging to the Macaronesian biogeographic region, located around 500 km off Senegal.Methods A total of 405 new specimens of Tarentola geckos were collected from nine islands with very different geological histories, topography, climate and habitats. Mitochondrial cytochrome b (cyt b) gene and 12S rRNA partial sequences were obtained and analysed using phylogenetic methods and networks to determine molecular diversity, demographic features and phylogeographic patterns.Results The phylogenetic relationships between all known forms of Cape Verdean Tarentola specimens were estimated for the first time, the relationships between new forms were assessed and previously hypothesized relationships were re‐examined. Despite the large sample size, low intraspecific diversity was found using a 303‐bp cyt b fragment. Star‐like haplotype networks and statistical tests suggest the past occurrence of a rapid demographic and geographical expansion over most of the islands. Genetic variability is positively correlated with size, elevation and habitat diversity of the islands, but is not linearly related to the age of the islands. Biogeographical patterns have, in general, high concordance with phylogenetic breaks and with the three eco‐geographical island groups. Volcanism and habitat diversity, both tightly linked with island ontogeny, as postulated by the general dynamic model of oceanic island biogeography, as well as present and historical size of the islands appear to be the main factors explaining the genetic diversity of this group.Main conclusions The Tarentola radiation was clarified and is clearly associated with the geological and ecological features of the islands. Two factors may account for the low intraspecific variation: (1) recent volcanic activity and high ecological stress, and (2) poor habitat diversity within some islands. More studies are needed to align taxonomy with phylogenetic relationships, whereas GIS modelling may help to predict precise species distributions.

Drivers of lowland rain forest community assembly, species diversity and forest structure on islands in the tropical South Pacific

Summary 1. Testing the comparative strength and influence of age and area of islands, proximity of source propagules and disturbances on community assembly, species diversity and vegetation structure has proved difficult at large scales. The little‐studied rain forests in the Tropical South Pacific (TSP) provide a unique study area to investigate determinants of community dynamics, with islands varying in age, isolation, area and cyclone frequency. We tested the effects of biogeographical factors and cyclone frequency on the species composition, species diversity and forest structure of old‐growth rain forest using 1‐ha inventory plots on 12 islands between New Guinea and the Solomon Islands. 2. As predicted by the General Dynamic Model of Oceanic Island Biogeography, the biogeographical variables of archipelago age and island area are the most important factors affecting species richness and diversity, with older and larger islands having higher richness and diversity. There is no significant effect of cyclone frequency on species diversity. 3. The theory that diversity drives endemism is not supported in this system as endemism is not correlated with species diversity. Instead, age and isolation of an island best explain patterns of endemism, with the latter suggesting dispersal limitations between archipelagos. 4. Proximity to source area influences species composition of lowland tropical rain forests in the TSP, which is also supported by a strong correlation between geographic distance and floristic similarity. Vector‐fitting onto non‐metric multidimensional scaling suggests that archipelago age and cyclone frequency may, in addition to proximity to source area, influence species composition. This implies that a species’ tolerance to cyclones affects its abundance at different cyclone frequencies. 5. Synthesis. Both biogeographical variables (island area and isolation) and cyclone frequency appear to affect community assembly in lowland rain forests in the TSP. While species are hence not ecologically equivalent, interspecific ecological differences do not seem to affect the overall patterns of species diversity, which are mostly determined by biogeographical variables, as predicted by the neutral theory.

On the general dynamic model of oceanic island biogeography

AbstractAim To investigate the biological meaning of equations used to apply the general dynamic model (GDM) of oceanic island biogeography proposed by R. J. Whittaker, K. A. Triantis and R. J. Ladle.Location Analyses are presented for 17 animal groups living on the Aeolian Islands, a volcanic archipelago in the central Mediterranean, near Sicily.Methods In addition to the mathematical implementation of the GDM proposed by Whittaker, Triantis and Ladle, and termed here logATT2 (, where S is species number or any other diversity metric, t is island age, A is island area, and a, b, c and d are fitted parameters), a new implementation based on the Arrhenius equation of the species–area relationship (SAR) is investigated. The new model (termed powerATT2) is: . For logATT2 and powerATT2 models, equations were developed to calculate (1) the expected number of species at equilibrium (i.e. when the island has reached maturity) per unit area (Seq), and (2) the time required to obtain this value (teq). Whereas the intercept in the Gleason model (S = C + z log A) or the coefficient of the Arrhenius power model (S = CAz) of the SAR can be considered measures of the expected number of species per unit area, this is not the case for the parameter a of the ATT2 models. However, values of Seq can be used for this purpose. The index of ‘colonization ability’ (CAB), calculated as the ratio , may provide a measure of the mean number of species added per unit area per unit time.Results Both ATT2 models fitted most of the data well, but the powerATT2 model was in most cases superior. Equilibrial values of species richness (Seq) varied from c. 3 species km−2 (reptiles) to 100 species km−2 (mites). The fitted curves for the powerATT2 model showed large variations in d, from 0.03 to 3. However, most groups had values of d around 0.2–0.4, as commonly observed for the z‐values of SARs modelled by a power function. Equilibration times ranged from about 170,000 years to 400,000 years. Mites and springtails had very high values of CAB, thus adding many more species per unit area per unit time than others. Reptiles and phytophagous scarabs showed very low values, being the groups that added fewest species per unit area per unit time.Main conclusions Values of equilibrial species richness per unit area are influenced by species biology (e.g. body size and ecological specialization). Theoretical and empirical evidence suggests that higher immigration rates should increase the z‐values of the Arrhenius model. Thus, in the same archipelago, groups with larger z‐values should be characterized by higher dispersal ability. Results obtained here for the parameter d conform to this prediction.

Time, area and isolation: factors driving the diversification of Azorean arthropods

AbstractAim R. J. Whittakeret al.recently proposed a ‘general dynamic model of oceanic island biogeography’ (GDM), providing a general explanation of island biodiversity patterns by relating fundamental biogeographical processes – speciation, immigration, extinction – to area (A) and time (T; maximum island geological age). We adapt their model, which predicts a positive relationship with area combined with a humped relationship to time (designated the ATT2model), to study the factors promoting diversification on the Azores for several arthropod groups.Location The Azorean archipelago (North Atlantic; 37–40° N, 25–31° W).Methods We use the number of single‐island endemics (SIEs) as a measure of diversification, to evaluate four different predictions for the variation in SIEs between different islands, derived from the GDM theory and our knowledge of the fauna and history of the Azores. We calculated the number of SIEs for seven out of the nine Azorean islands and six groups of species (all arthropods, beetles, cavernicolous and non‐cavernicolous species, and taxa with high and low dispersal abilities). Several variables accounting for island characteristics (area, geological age, habitat diversity and isolation) and generalized linear models were used to evaluate the reliability of each prediction.Results A linear and positive relationship between SIEs and an AT (area + time) model was the most parsimonious explanation for overall arthropod diversification. However, cavernicolous species showed the opposite pattern (more SIEs inhabiting the youngest islands). Also, isolation was an important predictor of diversification for all groups except for the species with high dispersal ability; while the former were negatively related to the distance from the main source of colonizing lineages (Santa Maria island in most cases), the latter were related to area. Dispersal ability was also a key factor affecting the diversification of most groups of species.Main conclusions In general, the diversification of Azorean arthropods is affected by age, area and isolation. However, different groups are affected by these factors in different ways, showing radically different patterns. Although the ATT2model fails to predict the diversification pattern of several groups, it provides a framework for integrating these deviations into a general theory. Further improvements of the GDM theory need to take into account the particular traits of each group and the role of isolation in shaping island diversity.

Is a new paradigm emerging for oceanic island biogeography?

AbstractFollowing several decades during which two dissimilar and incompatible models (equilibrium and vicariance) dominated island biogeography, recent publications have documented patterns that point the way towards a new paradigm that includes elements of both models, as well as some novel aspects. Many of these seminal contributions have been made possible by the recent development of robust, temporally calibrated phylogenies used in concert with increasingly precise and reliable geological reconstructions of oceanic regions. Although a new general model of oceanic island biogeography has not yet been proposed, in this brief overview I present six hypotheses that summarize aspects of the emerging paradigm. These hypotheses deal with: the frequency of dispersal over oceanic water barriers by terrestrial organisms; the existence of substantial variation in the amount of dispersal (and gene flow) within a given set of related species within a given archipelago; the frequency, extent and impact on species richness of diversification within archipelagos; the frequent correlation of island age and the age of the species that live on the island; the long‐term persistence of species on oceanic islands; and the occasional recolonization of continents by species from clades that diversified on islands. Identifying, testing, and seeking means of synthesizing these and other emerging hypotheses may allow a new conceptual paradigm to emerge.